{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "acf6d420-c8ca-4168-92e3-72bdae3ca620",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入所需库\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# 设置中文字体，避免可视化时中文乱码\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # Windows系统\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f82ad8f-2854-4831-b750-7ecc5916fc20",
   "metadata": {},
   "source": [
    "一、数据探查"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65de26c6-ad40-4c1f-a2f1-4aa5f8914ba2",
   "metadata": {},
   "source": [
    "1.1 数据合并：将指定文件夹下的9个CSV文件合并成1个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ae7c38c-fc87-4c04-a2ef-9579d5907048",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义文件路径\n",
    "file_path = r'C:\\Users\\86151\\Desktop\\jobs'\n",
    "all_df = []\n",
    "\n",
    "# 遍历文件夹下的所有CSV文件\n",
    "for filename in os.listdir(file_path):\n",
    "    if filename.endswith('.csv'):\n",
    "        # 读取单个CSV文件\n",
    "        temp_df = pd.read_csv(os.path.join(file_path, filename), encoding='gbk')\n",
    "        all_df.append(temp_df)\n",
    "# 合并所有数据并保存为新文件\n",
    "all_jobs_df = pd.concat(all_df, ignore_index=True)\n",
    "all_jobs_df.to_csv('all_jobs.csv', index=False, encoding='gbk')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b18fe6-7ba3-42eb-9ab9-03758cff6af3",
   "metadata": {},
   "source": [
    "1.2 读取汇总后的CSV文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "943e438f-4f65-4534-842c-2ebfb05f12de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_name</th>\n",
       "      <th>uri</th>\n",
       "      <th>salary</th>\n",
       "      <th>site</th>\n",
       "      <th>year</th>\n",
       "      <th>edu</th>\n",
       "      <th>job_name</th>\n",
       "      <th>city</th>\n",
       "      <th>job_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中国电信云</td>\n",
       "      <td>https://www.zhipin.com/job_detail/11266fc18dc1...</td>\n",
       "      <td>20-40K·17薪</td>\n",
       "      <td>北京 海淀区 西山</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>Python</td>\n",
       "      <td>beijing</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>奇虎360</td>\n",
       "      <td>https://www.zhipin.com/job_detail/2a3103941dc2...</td>\n",
       "      <td>10-20K·15薪</td>\n",
       "      <td>北京 朝阳区 酒仙桥</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>大专</td>\n",
       "      <td>python数据分析师</td>\n",
       "      <td>beijing</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VIPKID</td>\n",
       "      <td>https://www.zhipin.com/job_detail/2dd7f2760947...</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>北京 朝阳区 十里堡</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>Python</td>\n",
       "      <td>beijing</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>天阳科技</td>\n",
       "      <td>https://www.zhipin.com/job_detail/a0c8485a448b...</td>\n",
       "      <td>12-24K</td>\n",
       "      <td>北京 石景山区 八宝山</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>python工程师</td>\n",
       "      <td>beijing</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>武汉佰钧成</td>\n",
       "      <td>https://www.zhipin.com/job_detail/d6627bf7c1e2...</td>\n",
       "      <td>12-17K</td>\n",
       "      <td>北京 朝阳区 三元桥</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>大专</td>\n",
       "      <td>python开发</td>\n",
       "      <td>beijing</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9820</th>\n",
       "      <td>公众智能</td>\n",
       "      <td>https://www.zhipin.com/job_detail/7b9c08dbce81...</td>\n",
       "      <td>8-10K</td>\n",
       "      <td>西安</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>产品经理</td>\n",
       "      <td>xian</td>\n",
       "      <td>产品经理</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9821</th>\n",
       "      <td>微感</td>\n",
       "      <td>https://www.zhipin.com/job_detail/c7e99005528f...</td>\n",
       "      <td>8-10K</td>\n",
       "      <td>西安 雁塔区 紫薇田园都市</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>大专</td>\n",
       "      <td>产品经理</td>\n",
       "      <td>xian</td>\n",
       "      <td>产品经理</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9822</th>\n",
       "      <td>巴斯光年</td>\n",
       "      <td>https://www.zhipin.com/job_detail/1045fe64f248...</td>\n",
       "      <td>10-20K</td>\n",
       "      <td>西安 雁塔区 大雁塔</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>产品经理</td>\n",
       "      <td>xian</td>\n",
       "      <td>产品经理</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9823</th>\n",
       "      <td>西大华特科技</td>\n",
       "      <td>https://www.zhipin.com/job_detail/e3c21cc748e7...</td>\n",
       "      <td>5-8K</td>\n",
       "      <td>西安 雁塔区 唐延路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>产品经理（农药）</td>\n",
       "      <td>xian</td>\n",
       "      <td>产品经理</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9824</th>\n",
       "      <td>西安纯粹科技</td>\n",
       "      <td>https://www.zhipin.com/job_detail/09965129db3e...</td>\n",
       "      <td>3-6K</td>\n",
       "      <td>西安 雁塔区 玫瑰大楼</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>产品经理</td>\n",
       "      <td>xian</td>\n",
       "      <td>产品经理</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9825 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     company_name                                                uri  \\\n",
       "0           中国电信云  https://www.zhipin.com/job_detail/11266fc18dc1...   \n",
       "1           奇虎360  https://www.zhipin.com/job_detail/2a3103941dc2...   \n",
       "2          VIPKID  https://www.zhipin.com/job_detail/2dd7f2760947...   \n",
       "3            天阳科技  https://www.zhipin.com/job_detail/a0c8485a448b...   \n",
       "4           武汉佰钧成  https://www.zhipin.com/job_detail/d6627bf7c1e2...   \n",
       "...           ...                                                ...   \n",
       "9820         公众智能  https://www.zhipin.com/job_detail/7b9c08dbce81...   \n",
       "9821           微感  https://www.zhipin.com/job_detail/c7e99005528f...   \n",
       "9822         巴斯光年  https://www.zhipin.com/job_detail/1045fe64f248...   \n",
       "9823       西大华特科技  https://www.zhipin.com/job_detail/e3c21cc748e7...   \n",
       "9824       西安纯粹科技  https://www.zhipin.com/job_detail/09965129db3e...   \n",
       "\n",
       "          salary           site   year edu     job_name     city job_type  \n",
       "0     20-40K·17薪      北京 海淀区 西山   经验不限  本科       Python  beijing   python  \n",
       "1     10-20K·15薪     北京 朝阳区 酒仙桥   3-5年  大专  python数据分析师  beijing   python  \n",
       "2     20-40K·14薪     北京 朝阳区 十里堡  5-10年  本科       Python  beijing   python  \n",
       "3         12-24K    北京 石景山区 八宝山   1-3年  本科    python工程师  beijing   python  \n",
       "4         12-17K     北京 朝阳区 三元桥   3-5年  大专     python开发  beijing   python  \n",
       "...          ...            ...    ...  ..          ...      ...      ...  \n",
       "9820       8-10K           西安     3-5年  本科         产品经理     xian     产品经理  \n",
       "9821       8-10K  西安 雁塔区 紫薇田园都市   3-5年  大专         产品经理     xian     产品经理  \n",
       "9822      10-20K     西安 雁塔区 大雁塔   3-5年  本科         产品经理     xian     产品经理  \n",
       "9823        5-8K     西安 雁塔区 唐延路   1-3年  硕士     产品经理（农药）     xian     产品经理  \n",
       "9824        3-6K    西安 雁塔区 玫瑰大楼   1-3年  本科         产品经理     xian     产品经理  \n",
       "\n",
       "[9825 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  读取汇总后的CSV文件\n",
    "all_jobs_df = pd.read_csv('all_jobs.csv', encoding='gbk')\n",
    "all_jobs_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77448ce4-6bcf-4841-a047-25417a5082c2",
   "metadata": {},
   "source": [
    "1.3. 读取指定列并命名为job数据框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e1251bf5-650d-44c2-ab07-8e7b788287d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取指定列并命名为job数据框\n",
    "columns = ['company_name', 'salary', 'site', 'year', 'edu', 'job_name', 'job_type']\n",
    "job = all_jobs_df[columns].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd3168b3-cf71-4d32-92de-f0437a67efec",
   "metadata": {},
   "source": [
    "1.4 查看DataFrame全体数据摘要信息（info）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8948ed76-fac1-4041-ab3f-24b1eca6eb35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据摘要信息：\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9825 entries, 0 to 9824\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   company_name  9825 non-null   object\n",
      " 1   salary        9825 non-null   object\n",
      " 2   site          9825 non-null   object\n",
      " 3   year          9825 non-null   object\n",
      " 4   edu           9825 non-null   object\n",
      " 5   job_name      9825 non-null   object\n",
      " 6   job_type      9825 non-null   object\n",
      "dtypes: object(7)\n",
      "memory usage: 537.4+ KB\n",
      "None\n",
      "\n",
      "数据前5行：\n",
      "  company_name      salary         site   year edu     job_name job_type\n",
      "0        中国电信云  20-40K·17薪    北京 海淀区 西山   经验不限  本科       Python   python\n",
      "1        奇虎360  10-20K·15薪   北京 朝阳区 酒仙桥   3-5年  大专  python数据分析师   python\n",
      "2       VIPKID  20-40K·14薪   北京 朝阳区 十里堡  5-10年  本科       Python   python\n",
      "3         天阳科技      12-24K  北京 石景山区 八宝山   1-3年  本科    python工程师   python\n",
      "4        武汉佰钧成      12-17K   北京 朝阳区 三元桥   3-5年  大专     python开发   python\n"
     ]
    }
   ],
   "source": [
    "#查看DataFrame全体数据摘要信息\n",
    "print(\"数据摘要信息：\")\n",
    "print(job.info())\n",
    "print(\"\\n数据前5行：\")\n",
    "print(job.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beceab80-6f44-4a80-a82a-f019cb4dd4bc",
   "metadata": {},
   "source": [
    "二、数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2a27484-1043-412e-86e9-5ed3db076604",
   "metadata": {},
   "source": [
    "2.1 查看岗位类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "531627e0-acad-4a7e-b4b1-9b0ffa0f28e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "岗位类型分布：\n",
      "job_type\n",
      "java      2688\n",
      "产品经理      2688\n",
      "数据分析      2431\n",
      "python    2018\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 查看岗位类型\n",
    "print(\"\\n岗位类型分布：\")\n",
    "print(job['job_type'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c784a11-f1a3-4c09-8da7-64f0f2db137b",
   "metadata": {},
   "source": [
    "2.2 使用query函数筛选与数据分析相关的岗位类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "06b14be7-cc4c-46c3-9488-6bdb97f5dcdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>京东数字科技</td>\n",
       "      <td>20-40K</td>\n",
       "      <td>北京 大兴区 次渠</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>瑞幸咖啡</td>\n",
       "      <td>15-30K</td>\n",
       "      <td>北京 海淀区 知春路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>VIPKID</td>\n",
       "      <td>30-60K·14薪</td>\n",
       "      <td>北京 朝阳区 十里堡</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>京东集团</td>\n",
       "      <td>7-14K</td>\n",
       "      <td>北京</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析主管</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>18-25K·16薪</td>\n",
       "      <td>北京 朝阳区 太阳宫</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>博士</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9520</th>\n",
       "      <td>梦之童</td>\n",
       "      <td>8-9K</td>\n",
       "      <td>西安 新城区 解放路</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>大专</td>\n",
       "      <td>医学总监</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9521</th>\n",
       "      <td>平安普惠</td>\n",
       "      <td>5-10K</td>\n",
       "      <td>西安 雁塔区 唐延路</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>大专</td>\n",
       "      <td>风控</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9522</th>\n",
       "      <td>耳垂网络科技</td>\n",
       "      <td>15-18K</td>\n",
       "      <td>西安 雁塔区 小寨</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>算法研究员</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9523</th>\n",
       "      <td>森弗集团</td>\n",
       "      <td>3-6K</td>\n",
       "      <td>西安</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>IT技术支持</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9524</th>\n",
       "      <td>达创</td>\n",
       "      <td>5-8K</td>\n",
       "      <td>西安 未央区 未央路</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>学历不限</td>\n",
       "      <td>策划经理</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2431 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     company_name      salary        site   year   edu job_name job_type\n",
       "577        京东数字科技      20-40K   北京 大兴区 次渠   3-5年    本科     数据分析     数据分析\n",
       "578          瑞幸咖啡      15-30K  北京 海淀区 知春路   1-3年    本科    数据分析师     数据分析\n",
       "579        VIPKID  30-60K·14薪  北京 朝阳区 十里堡  5-10年    本科   数据分析专家     数据分析\n",
       "580          京东集团       7-14K        北京     3-5年    本科   数据分析主管     数据分析\n",
       "581        BOSS直聘  18-25K·16薪  北京 朝阳区 太阳宫   1-3年    博士     数据分析     数据分析\n",
       "...           ...         ...         ...    ...   ...      ...      ...\n",
       "9520          梦之童        8-9K  西安 新城区 解放路   1年以内    大专     医学总监     数据分析\n",
       "9521         平安普惠       5-10K  西安 雁塔区 唐延路   1年以内    大专       风控     数据分析\n",
       "9522       耳垂网络科技      15-18K   西安 雁塔区 小寨   1-3年    本科    算法研究员     数据分析\n",
       "9523         森弗集团        3-6K        西安     经验不限    本科   IT技术支持     数据分析\n",
       "9524           达创        5-8K  西安 未央区 未央路   1年以内  学历不限     策划经理     数据分析\n",
       "\n",
       "[2431 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用query函数筛选与数据分析相关的岗位类型\n",
    "job = job.query(\"job_type.str.contains('数据分析', na=False)\", engine='python')\n",
    "job"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c3b3eaf-b6e6-4b9a-be9c-1e92d5cf6b50",
   "metadata": {},
   "source": [
    "2.3 筛选出python和数据分析的岗位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4b3471ce-672f-4238-b5df-3aa363e7658e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_name</th>\n",
       "      <th>salary</th>\n",
       "      <th>site</th>\n",
       "      <th>year</th>\n",
       "      <th>edu</th>\n",
       "      <th>job_name</th>\n",
       "      <th>job_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>京东数字科技</td>\n",
       "      <td>20-40K</td>\n",
       "      <td>北京 大兴区 次渠</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>瑞幸咖啡</td>\n",
       "      <td>15-30K</td>\n",
       "      <td>北京 海淀区 知春路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>VIPKID</td>\n",
       "      <td>30-60K·14薪</td>\n",
       "      <td>北京 朝阳区 十里堡</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>京东集团</td>\n",
       "      <td>7-14K</td>\n",
       "      <td>北京</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析主管</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>18-25K·16薪</td>\n",
       "      <td>北京 朝阳区 太阳宫</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>博士</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9393</th>\n",
       "      <td>直信立兴</td>\n",
       "      <td>2-4K</td>\n",
       "      <td>西安 未央区 未央路</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>大专</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9394</th>\n",
       "      <td>思普瑞数码</td>\n",
       "      <td>8-9K</td>\n",
       "      <td>西安 雁塔区 沣惠南路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9395</th>\n",
       "      <td>中邮速递易</td>\n",
       "      <td>6-8K</td>\n",
       "      <td>西安 雁塔区 唐延路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9396</th>\n",
       "      <td>斯维智能</td>\n",
       "      <td>5-9K</td>\n",
       "      <td>西安 雁塔区 高新路</td>\n",
       "      <td>应届生</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9397</th>\n",
       "      <td>长安信托</td>\n",
       "      <td>6-8K</td>\n",
       "      <td>西安 雁塔区 高新路</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>学历不限</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1971 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     company_name      salary         site   year   edu job_name job_type\n",
       "577        京东数字科技      20-40K    北京 大兴区 次渠   3-5年    本科     数据分析     数据分析\n",
       "578          瑞幸咖啡      15-30K   北京 海淀区 知春路   1-3年    本科    数据分析师     数据分析\n",
       "579        VIPKID  30-60K·14薪   北京 朝阳区 十里堡  5-10年    本科   数据分析专家     数据分析\n",
       "580          京东集团       7-14K         北京     3-5年    本科   数据分析主管     数据分析\n",
       "581        BOSS直聘  18-25K·16薪   北京 朝阳区 太阳宫   1-3年    博士     数据分析     数据分析\n",
       "...           ...         ...          ...    ...   ...      ...      ...\n",
       "9393         直信立兴        2-4K   西安 未央区 未央路   1年以内    大专    数据分析师     数据分析\n",
       "9394        思普瑞数码        8-9K  西安 雁塔区 沣惠南路   1-3年    本科    数据分析师     数据分析\n",
       "9395        中邮速递易        6-8K   西安 雁塔区 唐延路   1-3年    本科    数据分析师     数据分析\n",
       "9396         斯维智能        5-9K   西安 雁塔区 高新路    应届生    本科    数据分析师     数据分析\n",
       "9397         长安信托        6-8K   西安 雁塔区 高新路   经验不限  学历不限    数据分析师     数据分析\n",
       "\n",
       "[1971 rows x 7 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 筛选出包含python和数据分析的岗位（job_name中包含这两个关键词）\n",
    "job = job.query(\"job_name.str.contains('python|数据分析', na=False, case=False)\", engine='python')\n",
    "job"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d5a39a9-a5b2-4ee0-ad1e-1f5ace597aff",
   "metadata": {},
   "source": [
    "2.4 统一job_name字段的大小写（筛选数据的时候不会因为大小写问题出错）\n",
    "job.job_name = job.job_name.str.title()\n",
    "job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "776a730c-4f58-443a-bfaf-18d776829a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "#创建副本\n",
    "job_copy=job.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "751a4683-a286-4afd-b425-762b2113fac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_name</th>\n",
       "      <th>salary</th>\n",
       "      <th>site</th>\n",
       "      <th>year</th>\n",
       "      <th>edu</th>\n",
       "      <th>job_name</th>\n",
       "      <th>job_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>京东数字科技</td>\n",
       "      <td>20-40K</td>\n",
       "      <td>北京 大兴区 次渠</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>瑞幸咖啡</td>\n",
       "      <td>15-30K</td>\n",
       "      <td>北京 海淀区 知春路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>VIPKID</td>\n",
       "      <td>30-60K·14薪</td>\n",
       "      <td>北京 朝阳区 十里堡</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>京东集团</td>\n",
       "      <td>7-14K</td>\n",
       "      <td>北京</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析主管</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>18-25K·16薪</td>\n",
       "      <td>北京 朝阳区 太阳宫</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>博士</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9393</th>\n",
       "      <td>直信立兴</td>\n",
       "      <td>2-4K</td>\n",
       "      <td>西安 未央区 未央路</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>大专</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9394</th>\n",
       "      <td>思普瑞数码</td>\n",
       "      <td>8-9K</td>\n",
       "      <td>西安 雁塔区 沣惠南路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9395</th>\n",
       "      <td>中邮速递易</td>\n",
       "      <td>6-8K</td>\n",
       "      <td>西安 雁塔区 唐延路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9396</th>\n",
       "      <td>斯维智能</td>\n",
       "      <td>5-9K</td>\n",
       "      <td>西安 雁塔区 高新路</td>\n",
       "      <td>应届生</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9397</th>\n",
       "      <td>长安信托</td>\n",
       "      <td>6-8K</td>\n",
       "      <td>西安 雁塔区 高新路</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>学历不限</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1971 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     company_name      salary         site   year   edu job_name job_type\n",
       "577        京东数字科技      20-40K    北京 大兴区 次渠   3-5年    本科     数据分析     数据分析\n",
       "578          瑞幸咖啡      15-30K   北京 海淀区 知春路   1-3年    本科    数据分析师     数据分析\n",
       "579        VIPKID  30-60K·14薪   北京 朝阳区 十里堡  5-10年    本科   数据分析专家     数据分析\n",
       "580          京东集团       7-14K         北京     3-5年    本科   数据分析主管     数据分析\n",
       "581        BOSS直聘  18-25K·16薪   北京 朝阳区 太阳宫   1-3年    博士     数据分析     数据分析\n",
       "...           ...         ...          ...    ...   ...      ...      ...\n",
       "9393         直信立兴        2-4K   西安 未央区 未央路   1年以内    大专    数据分析师     数据分析\n",
       "9394        思普瑞数码        8-9K  西安 雁塔区 沣惠南路   1-3年    本科    数据分析师     数据分析\n",
       "9395        中邮速递易        6-8K   西安 雁塔区 唐延路   1-3年    本科    数据分析师     数据分析\n",
       "9396         斯维智能        5-9K   西安 雁塔区 高新路    应届生    本科    数据分析师     数据分析\n",
       "9397         长安信托        6-8K   西安 雁塔区 高新路   经验不限  学历不限    数据分析师     数据分析\n",
       "\n",
       "[1971 rows x 7 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  统一job_name字段的大小写（Title格式：首字母大写，其余小写）\n",
    "job_copy.job_name = job.job_name.str.title()\n",
    "job"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e52517-a6ac-4f19-9879-ce0a70370a48",
   "metadata": {},
   "source": [
    "2.5 根据“job_name”列筛选出“数据分析”关键词的岗位，包含“数据分析”关键词的岗位。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e7af2494-01f1-47ca-969d-52f7c1bca815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_name</th>\n",
       "      <th>salary</th>\n",
       "      <th>site</th>\n",
       "      <th>year</th>\n",
       "      <th>edu</th>\n",
       "      <th>job_name</th>\n",
       "      <th>job_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>京东数字科技</td>\n",
       "      <td>20-40K</td>\n",
       "      <td>北京 大兴区 次渠</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>瑞幸咖啡</td>\n",
       "      <td>15-30K</td>\n",
       "      <td>北京 海淀区 知春路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>VIPKID</td>\n",
       "      <td>30-60K·14薪</td>\n",
       "      <td>北京 朝阳区 十里堡</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>京东集团</td>\n",
       "      <td>7-14K</td>\n",
       "      <td>北京</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析主管</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>18-25K·16薪</td>\n",
       "      <td>北京 朝阳区 太阳宫</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>博士</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9393</th>\n",
       "      <td>直信立兴</td>\n",
       "      <td>2-4K</td>\n",
       "      <td>西安 未央区 未央路</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>大专</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9394</th>\n",
       "      <td>思普瑞数码</td>\n",
       "      <td>8-9K</td>\n",
       "      <td>西安 雁塔区 沣惠南路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9395</th>\n",
       "      <td>中邮速递易</td>\n",
       "      <td>6-8K</td>\n",
       "      <td>西安 雁塔区 唐延路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9396</th>\n",
       "      <td>斯维智能</td>\n",
       "      <td>5-9K</td>\n",
       "      <td>西安 雁塔区 高新路</td>\n",
       "      <td>应届生</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9397</th>\n",
       "      <td>长安信托</td>\n",
       "      <td>6-8K</td>\n",
       "      <td>西安 雁塔区 高新路</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>学历不限</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1967 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     company_name      salary         site   year   edu job_name job_type\n",
       "577        京东数字科技      20-40K    北京 大兴区 次渠   3-5年    本科     数据分析     数据分析\n",
       "578          瑞幸咖啡      15-30K   北京 海淀区 知春路   1-3年    本科    数据分析师     数据分析\n",
       "579        VIPKID  30-60K·14薪   北京 朝阳区 十里堡  5-10年    本科   数据分析专家     数据分析\n",
       "580          京东集团       7-14K         北京     3-5年    本科   数据分析主管     数据分析\n",
       "581        BOSS直聘  18-25K·16薪   北京 朝阳区 太阳宫   1-3年    博士     数据分析     数据分析\n",
       "...           ...         ...          ...    ...   ...      ...      ...\n",
       "9393         直信立兴        2-4K   西安 未央区 未央路   1年以内    大专    数据分析师     数据分析\n",
       "9394        思普瑞数码        8-9K  西安 雁塔区 沣惠南路   1-3年    本科    数据分析师     数据分析\n",
       "9395        中邮速递易        6-8K   西安 雁塔区 唐延路   1-3年    本科    数据分析师     数据分析\n",
       "9396         斯维智能        5-9K   西安 雁塔区 高新路    应届生    本科    数据分析师     数据分析\n",
       "9397         长安信托        6-8K   西安 雁塔区 高新路   经验不限  学历不限    数据分析师     数据分析\n",
       "\n",
       "[1967 rows x 7 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  再次筛选出包含\"数据分析\"关键词的岗位（双重保险）\n",
    "job = job[job['job_name'].str.contains('数据分析', na=False)]\n",
    "job"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42fb1f8d-8f8e-4032-a907-cca482be77b8",
   "metadata": {},
   "source": [
    "2.6 由于需要提取出各个城市的名称，用于统计各个城市关于“数据分析”岗位相关信息，所以将“site”字段拆分成三个列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "46eabc94-6547-4bb0-be74-041d2a45048d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_name</th>\n",
       "      <th>salary</th>\n",
       "      <th>site</th>\n",
       "      <th>year</th>\n",
       "      <th>edu</th>\n",
       "      <th>job_name</th>\n",
       "      <th>job_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>京东数字科技</td>\n",
       "      <td>20-40K</td>\n",
       "      <td>北京 大兴区 次渠</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>瑞幸咖啡</td>\n",
       "      <td>15-30K</td>\n",
       "      <td>北京 海淀区 知春路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>VIPKID</td>\n",
       "      <td>30-60K·14薪</td>\n",
       "      <td>北京 朝阳区 十里堡</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>京东集团</td>\n",
       "      <td>7-14K</td>\n",
       "      <td>北京</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析主管</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>18-25K·16薪</td>\n",
       "      <td>北京 朝阳区 太阳宫</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>博士</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9393</th>\n",
       "      <td>直信立兴</td>\n",
       "      <td>2-4K</td>\n",
       "      <td>西安 未央区 未央路</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>大专</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9394</th>\n",
       "      <td>思普瑞数码</td>\n",
       "      <td>8-9K</td>\n",
       "      <td>西安 雁塔区 沣惠南路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9395</th>\n",
       "      <td>中邮速递易</td>\n",
       "      <td>6-8K</td>\n",
       "      <td>西安 雁塔区 唐延路</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9396</th>\n",
       "      <td>斯维智能</td>\n",
       "      <td>5-9K</td>\n",
       "      <td>西安 雁塔区 高新路</td>\n",
       "      <td>应届生</td>\n",
       "      <td>本科</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9397</th>\n",
       "      <td>长安信托</td>\n",
       "      <td>6-8K</td>\n",
       "      <td>西安 雁塔区 高新路</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>学历不限</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1967 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     company_name      salary         site   year   edu job_name job_type\n",
       "577        京东数字科技      20-40K    北京 大兴区 次渠   3-5年    本科     数据分析     数据分析\n",
       "578          瑞幸咖啡      15-30K   北京 海淀区 知春路   1-3年    本科    数据分析师     数据分析\n",
       "579        VIPKID  30-60K·14薪   北京 朝阳区 十里堡  5-10年    本科   数据分析专家     数据分析\n",
       "580          京东集团       7-14K         北京     3-5年    本科   数据分析主管     数据分析\n",
       "581        BOSS直聘  18-25K·16薪   北京 朝阳区 太阳宫   1-3年    博士     数据分析     数据分析\n",
       "...           ...         ...          ...    ...   ...      ...      ...\n",
       "9393         直信立兴        2-4K   西安 未央区 未央路   1年以内    大专    数据分析师     数据分析\n",
       "9394        思普瑞数码        8-9K  西安 雁塔区 沣惠南路   1-3年    本科    数据分析师     数据分析\n",
       "9395        中邮速递易        6-8K   西安 雁塔区 唐延路   1-3年    本科    数据分析师     数据分析\n",
       "9396         斯维智能        5-9K   西安 雁塔区 高新路    应届生    本科    数据分析师     数据分析\n",
       "9397         长安信托        6-8K   西安 雁塔区 高新路   经验不限  学历不限    数据分析师     数据分析\n",
       "\n",
       "[1967 rows x 7 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将site字段拆分成三个列（假设site格式为\"城市-区域-街道\"，按\"-\"分割）\n",
    "job_copy[['city', 'district', 'street']] = job['site'].str.split(' ', n=2, expand=True)\n",
    "job"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84819cf6-3b9b-4f04-83ba-4fa636c300d1",
   "metadata": {},
   "source": [
    "2.7 抽取“salary”列中的最大最小值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "15a253b9-6171-43d4-902b-c93f3bf7c584",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "预处理后的数据前5行（包含薪资范围）：\n",
      "    company_name city edu job_name  salary_min  salary_max  salary_avg\n",
      "577       京东数字科技   北京  本科     数据分析        20.0        40.0        30.0\n",
      "578         瑞幸咖啡   北京  本科    数据分析师        15.0        30.0        22.5\n",
      "579       VIPKID   北京  本科   数据分析专家        30.0        60.0        45.0\n",
      "580         京东集团   北京  本科   数据分析主管         7.0        14.0        10.5\n",
      "581       BOSS直聘   北京  博士     数据分析        18.0        25.0        21.5\n",
      "\n",
      "薪资统计信息：\n",
      "最高薪资上限: 90.0k\n",
      "最低薪资下限: 1.0k\n",
      "平均薪资: 16.52k\n"
     ]
    }
   ],
   "source": [
    "# 定义函数提取薪资上下限\n",
    "def extract_salary(salary_str):\n",
    "    if pd.isna(salary_str):\n",
    "        return None, None\n",
    "    # 去除非数字和\"-\"的字符，提取薪资范围\n",
    "    import re\n",
    "    nums = re.findall(r'\\d+', str(salary_str))\n",
    "    if len(nums) >= 2:\n",
    "        return int(nums[0]), int(nums[1])\n",
    "    return None, None\n",
    "\n",
    "# 应用函数获取薪资上下限\n",
    "job_copy[['salary_min', 'salary_max']] = job['salary'].apply(lambda x: pd.Series(extract_salary(x)))\n",
    "\n",
    "# 计算平均薪资\n",
    "job_copy['salary_avg'] = (job_copy['salary_min'] + job_copy['salary_max']) / 2\n",
    "\n",
    "print(\"\\n预处理后的数据前5行（包含薪资范围）：\")\n",
    "print(job_copy[['company_name', 'city', 'edu', 'job_name', 'salary_min', 'salary_max', 'salary_avg']].head())\n",
    "\n",
    "# 查看整个数据集的薪资统计信息\n",
    "print(\"\\n薪资统计信息：\")\n",
    "print(f\"最高薪资上限: {job_copy['salary_max'].max()}k\")\n",
    "print(f\"最低薪资下限: {job_copy['salary_min'].min()}k\")\n",
    "print(f\"平均薪资: {job_copy['salary_avg'].mean():.2f}k\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f12ba8a-f64d-47bf-833e-3e747fa36c97",
   "metadata": {},
   "source": [
    "三、数据分析及可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43465efe-b69c-4f79-b29e-0171934ce3c0",
   "metadata": {},
   "source": [
    "3.1 统计每个城市有多少个公司招聘“数据分析”岗位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0d9a3f55-fcd7-4371-b3cf-99e6449a97f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "各城市招聘公司数量：\n",
      "city\n",
      "上海    199\n",
      "广州    197\n",
      "北京    180\n",
      "杭州    180\n",
      "深圳    164\n",
      "南京    103\n",
      "成都     92\n",
      "武汉     81\n",
      "西安     51\n",
      "Name: company_name, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "图表已保存至: C:\\Users\\86151\\各城市数据分析岗位招聘公司数量.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 统计每个城市招聘数据分析岗位的公司数量\n",
    "# 确保 groupby 和后续操作都在 job_copy 上进行\n",
    "city_company_count = job_copy.groupby('city')['company_name'].nunique().sort_values(ascending=False)\n",
    "print(\"\\n各城市招聘公司数量：\")\n",
    "print(city_company_count)\n",
    "\n",
    "# 绘制城市公司数量柱状图\n",
    "plt.figure(figsize=(12, 6))\n",
    "city_company_count.plot(kind='bar', color='#1f77b4')\n",
    "plt.title('各城市数据分析岗位招聘公司数量', fontsize=14)\n",
    "plt.xlabel('城市', fontsize=12)\n",
    "plt.ylabel('公司数量', fontsize=12)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ffe2945-6ccc-46fb-bb81-b5b2577485db",
   "metadata": {},
   "source": [
    "3.2 为了更好地了解“数据分析”岗位的学历要求，需要了解不同学历的占比情况。\n",
    "（1）查看“edu”列数据的学历类型\n",
    "（2）整合“edu”列数据类型将“高中”和“中专”合并到“学历不限”\n",
    "（3）统计不同学历岗位的薪资并保留1位小数\n",
    "（4）统计不同学历岗位的公司需求量\n",
    "（5）绘制“数据分析”岗位不同学历图."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "24e5d4c9-c487-4a6b-8ee7-b8afd06de925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "原始学历类型分布：\n",
      "edu\n",
      "本科      1509\n",
      "大专       269\n",
      "硕士       114\n",
      "学历不限      62\n",
      "博士         5\n",
      "高中         4\n",
      "中专         4\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# （1）查看学历类型\n",
    "print(\"\\n原始学历类型分布：\")\n",
    "print(job['edu'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3fe0fc1d-7975-4777-8315-1a54ee6c5028",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in c:\\users\\86151\\anaconda3\\lib\\site-packages (5.24.1)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\86151\\anaconda3\\lib\\site-packages (from plotly) (8.2.3)\n",
      "Requirement already satisfied: packaging in c:\\users\\86151\\anaconda3\\lib\\site-packages (from plotly) (24.1)\n"
     ]
    }
   ],
   "source": [
    "pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1760b325-10b5-4533-a57d-0c043794d194",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\86151\\appdata\\roaming\\python\\python312\\site-packages (3.10.7)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\86151\\anaconda3\\lib\\site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\86151\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\86151\\anaconda3\\lib\\site-packages (from matplotlib) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\86151\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: numpy>=1.23 in c:\\users\\86151\\anaconda3\\lib\\site-packages (from matplotlib) (1.26.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\86151\\anaconda3\\lib\\site-packages (from matplotlib) (24.1)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\86151\\anaconda3\\lib\\site-packages (from matplotlib) (10.4.0)\n",
      "Requirement already satisfied: pyparsing>=3 in c:\\users\\86151\\anaconda3\\lib\\site-packages (from matplotlib) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\86151\\anaconda3\\lib\\site-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\86151\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "pip install --upgrade matplotlib --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b3ec73b5-2fd2-4bf1-a1ac-08327192fa5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42035bd1-465f-4e75-937c-a3a473e81960",
   "metadata": {},
   "source": [
    "分类数据的计数对比，柱状图 是最直观，最贴切的选择，它可以清晰地展示不同学历类型对应的岗位数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fc24db8f-7be2-4855-81bc-bffccaa0a8a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7405ff9b-5549-42f1-945d-4bd51fdd5b66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "原始学历类型分布：\n",
      "edu\n",
      "本科      1509\n",
      "大专       269\n",
      "硕士       114\n",
      "学历不限      62\n",
      "博士         5\n",
      "高中         4\n",
      "中专         4\n",
      "Name: count, dtype: int64\n",
      "\n",
      "图表已保存至: C:\\Users\\86151\\原始学历分布_仿照风格.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "# --- 全局设置：解决中文显示问题 ---\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 'SimHei' 是黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 解决坐标轴负号显示问题\n",
    "\n",
    "# （1）查看学历类型并打印分布\n",
    "print(\"\\n原始学历类型分布：\")\n",
    "edu_counts = job['edu'].value_counts(dropna=False)  # dropna=False 会把 NaN 也统计进去\n",
    "print(edu_counts)\n",
    "\n",
    "# 处理索引中的 None 值，避免绘图报错\n",
    "edu_counts_cleaned = edu_counts.rename(index={None: '未知'})\n",
    "cities = edu_counts_cleaned.index.tolist()  # 学历类型作为x轴（对应原代码的城市）\n",
    "population = edu_counts_cleaned.values.tolist()  # 岗位数量作为y轴（对应原代码的人口）\n",
    "n_bars = len(cities)\n",
    "\n",
    "# --- （2）生成可视化图表（仿照城市人口条形图风格） ---\n",
    "# 设置颜色和样式（数量适配学历类型数量，超出部分循环使用）\n",
    "colors = ['#FFE6E6', '#E6F7F7', '#E6F3FF', '#F0F7E6', '#FFF9E6', '#FFE6F3', '#F3E6FF', '#E6E6FF']\n",
    "patterns = ['/', 'o', 'x', '\\\\', '+', 'O', '*', '.', '-', '|']  # 填充图案\n",
    "inner_markers = ['o', 's', '^', 'D', 'v', '<', '>', 'p']  # 柱子内部图形\n",
    "marker_colors = ['#FF3333', '#33CCCC', '#3399FF', '#66CC66', '#FFCC33', '#FF66CC', '#9966FF', '#6666FF']\n",
    "marker_spacing = 0.9  # 图形间距系数\n",
    "\n",
    "# 创建条形图（沿用原代码的plt.bar风格，而非ax对象）\n",
    "plt.figure(figsize=(12, 8))\n",
    "bars = plt.bar(cities, population, color=colors[:n_bars], hatch=patterns[:n_bars], edgecolor='gray', linewidth=1)\n",
    "\n",
    "# 添加数据标签（仿照原代码位置和格式）\n",
    "for bar, pop in zip(bars, population):\n",
    "    plt.text(\n",
    "        bar.get_x() + bar.get_width() / 2 - 0.1,  # 水平居中微调\n",
    "        bar.get_height() + 0.2,  # 标签在柱子顶部上方\n",
    "        str(int(pop)),  # 显示岗位数量（整数）\n",
    "        fontsize=10,\n",
    "        fontweight='bold'\n",
    "    )\n",
    "\n",
    "# 关键步骤：在柱子内部填满图形（适配新风格）\n",
    "for i, (bar, marker, m_color) in enumerate(zip(bars, inner_markers[:n_bars], marker_colors[:n_bars])):\n",
    "    bar_x = bar.get_x() + bar.get_width() / 2.\n",
    "    bar_height = bar.get_height()\n",
    "    # 根据柱子高度计算图形数量\n",
    "    marker_count = int(bar_height) if bar_height >= 1 else 1\n",
    "    # 生成均匀分布的垂直位置\n",
    "    y_positions = np.linspace(marker_spacing, bar_height - marker_spacing, marker_count)\n",
    "    # 绘制填满柱子的图形\n",
    "    for y_pos in y_positions:\n",
    "        plt.plot(\n",
    "            bar_x,\n",
    "            y_pos,\n",
    "            marker=marker,\n",
    "            markersize=8,\n",
    "            color=m_color,\n",
    "            markerfacecolor=m_color,\n",
    "            markeredgecolor='white',\n",
    "            markeredgewidth=0.8\n",
    "        )\n",
    "\n",
    "# 添加标题和标签（修改为学历分布相关）\n",
    "plt.title('原始学历类型岗位分布（柱子内部填满图形）', fontsize=16)\n",
    "plt.xlabel('学历类型', fontsize=12)\n",
    "plt.ylabel('岗位数量', fontsize=12)\n",
    "\n",
    "# 自定义图例（仿照原代码逻辑，适配学历类型数量）\n",
    "legend_handles = [plt.Rectangle((0, 0), 1, 1, color=colors[i], hatch=patterns[i]) for i in range(n_bars)]\n",
    "plt.legend(legend_handles, cities, loc='upper right', title='学历类型')  # 图例显示学历类型\n",
    "\n",
    "# 设置Y轴刻度（仿照原代码预留顶部空间）\n",
    "plt.ylim(0, max(population) + 1)\n",
    "\n",
    "# 旋转X轴标签避免重叠（启用原代码注释的功能）\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "# 添加网格线（沿用原代码的y轴虚线网格）\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# 调整布局并保存、显示图表（完全沿用原代码流程）\n",
    "plt.tight_layout()\n",
    "# 保存图表到home目录（适配原代码保存格式）\n",
    "save_path = os.path.join(os.path.expanduser(\"~\"), \"原始学历分布_仿照风格.png\")\n",
    "plt.savefig(save_path, dpi=600, bbox_inches='tight')\n",
    "print(f\"\\n图表已保存至: {save_path}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "26875f4e-9740-4a59-b0ee-9055b28516f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "合并前的学历分布：\n",
      "edu\n",
      "本科      1513\n",
      "大专       269\n",
      "硕士       114\n",
      "学历不限      62\n",
      "博士         5\n",
      "高中         4\n",
      "中专         4\n",
      "Name: count, dtype: int64\n",
      "\n",
      "合并后的学历分布 (edu_merged)：\n",
      "edu_merged\n",
      "本科      1513\n",
      "大专       269\n",
      "硕士       114\n",
      "学历不限      70\n",
      "博士         5\n",
      "Name: count, dtype: int64\n",
      "\n",
      "查看合并效果（前10行）：\n",
      "    edu edu_merged\n",
      "577  本科         本科\n",
      "578  本科         本科\n",
      "579  本科         本科\n",
      "580  本科         本科\n",
      "581  博士         博士\n",
      "582  大专         大专\n",
      "583  博士         博士\n",
      "584  大专         大专\n",
      "585  本科         本科\n",
      "586  本科         本科\n",
      "\n",
      "详细统计：\n",
      "原本 '高中' 的数量: 4\n",
      "原本 '中专' 的数量: 4\n",
      "原本缺失 (NaN) 的数量: 0\n",
      "合并后 '学历不限' 的总数量: 70\n"
     ]
    }
   ],
   "source": [
    "# （2）整合学历类型：高中、中专合并到学历不限\n",
    "edu_mapping = {\n",
    "    '高中': '学历不限',\n",
    "    '中专': '学历不限'\n",
    "}\n",
    "job_copy['edu_merged'] = job_copy['edu'].replace(edu_mapping).fillna('学历不限')\n",
    "\n",
    "# 1. 查看合并前后的学历分布对比\n",
    "print(\"合并前的学历分布：\")\n",
    "print(job_copy['edu'].value_counts(dropna=False)) # dropna=False 会显示 NaN 的数量\n",
    "\n",
    "print(\"\\n合并后的学历分布 (edu_merged)：\")\n",
    "print(job_copy['edu_merged'].value_counts())\n",
    "\n",
    "# 2. 查看几行具体的数据，直观感受合并效果\n",
    "print(\"\\n查看合并效果（前10行）：\")\n",
    "# 选择几行数据，对比原始 edu 和合并后的 edu_merged\n",
    "sample_view = job_copy[['edu', 'edu_merged']].head(10)\n",
    "print(sample_view)\n",
    "\n",
    "# 3. 更详细的统计，可以查看转换的记录数\n",
    "original_counts = job_copy['edu'].value_counts()\n",
    "merged_counts = job_copy['edu_merged'].value_counts()\n",
    "\n",
    "print(\"\\n详细统计：\")\n",
    "print(f\"原本 '高中' 的数量: {original_counts.get('高中', 0)}\")\n",
    "print(f\"原本 '中专' 的数量: {original_counts.get('中专', 0)}\")\n",
    "print(f\"原本缺失 (NaN) 的数量: {job_copy['edu'].isna().sum()}\")\n",
    "print(f\"合并后 '学历不限' 的总数量: {merged_counts.get('学历不限', 0)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7cebdc13-55f1-400c-b82b-16b6417cfc36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "合并前的学历分布：\n",
      "edu\n",
      "本科      1513\n",
      "大专       269\n",
      "硕士       114\n",
      "学历不限      62\n",
      "博士         5\n",
      "高中         4\n",
      "中专         4\n",
      "Name: count, dtype: int64\n",
      "\n",
      "合并后的学历分布 (edu_merged)：\n",
      "edu_merged\n",
      "本科      1513\n",
      "大专       269\n",
      "硕士       114\n",
      "学历不限      70\n",
      "博士         5\n",
      "Name: count, dtype: int64\n",
      "\n",
      "查看合并效果（前10行）：\n",
      "    edu edu_merged\n",
      "577  本科         本科\n",
      "578  本科         本科\n",
      "579  本科         本科\n",
      "580  本科         本科\n",
      "581  博士         博士\n",
      "582  大专         大专\n",
      "583  博士         博士\n",
      "584  大专         大专\n",
      "585  本科         本科\n",
      "586  本科         本科\n",
      "\n",
      "详细统计：\n",
      "原本 '高中' 的数量: 4\n",
      "原本 '中专' 的数量: 4\n",
      "原本缺失 (NaN) 的数量: 0\n",
      "合并后 '学历不限' 的总数量: 70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86151\\AppData\\Local\\Temp\\ipykernel_28248\\627071665.py:71: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax1.set_xticklabels(ax1.get_xticklabels(), ha='right')\n",
      "C:\\Users\\86151\\AppData\\Local\\Temp\\ipykernel_28248\\627071665.py:100: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax2.set_xticklabels(ax2.get_xticklabels(), ha='right')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "对比图表已保存至: C:\\Users\\86151\\学历合并前后分布对比图.png\n"
     ]
    },
    {
     "data": {
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YRaAAAAAAYsvU8eMpNCg0AAAAIh6lRgwiUAAAAACxJz0lJSzHJX94UGgAAAAEB6VGjCFQAAAAADAL+cODQgMAACB4KDViCIECAAAAgFnIHx4UGgAAAMFFqREjCBQAAAAAzEL+8KDQAAAACD5KjRhAoAAAAABgFvKHB4UGAABAaFBqRDkCBQAAAACzkD88KDQAAABCh1IjihEoAAAAAJiF/OFBoQEAABBalBpRikABAAAAwCzkDw8KDQAAgNCj1IhCBAoAAAAAZiF/eFBoAAAAmINSI8oQKAAAAACYhfzhQaEBAABgHkqNKEKgAAAAAGAW8ocHhQYAAIC5KDWiBIECAAAAgFnIHx4UGgAAAOaj1IgCBAoAAAAAZiF/eFBoAAAAhAelRoQjUHjU1debfkwAAAAg1pA/PCg0AAAAwodSI4IRKDxq6+q0Ni/P9OMCAAAAsYT84UGhAQAAEF6UGhGKQOHhDRQVTqfpxwYAAABiBfnDg0IDAAAg/Cg1IhCBwqNpoJg6frzpxwcAAABiAfnDg0IDAADAGuLCvQF0DIHCo3mgsNvp5wAAAIBgI394UGgAAABYB78JjiAECg8CBQAAABB65A8P8gcAAIC1UGpECAKFB4ECAAAACD3yhwf5AwAAwHooNSIAgcKDQAEAAACEHvnDg/wBAABgTZQaFkeg8CBQAAAAAKFH/vAgfwAAAFgXHxRuYQVFRcovKiJQECgAAACAkKPQ8CB/AAAAWBtXalgYhQaBAgAAADADhYaHVfJHQVFRWI4LAAAQCSg1LCwnO5tAYYFAAQAAAEQzCg0Pq+SPdwsKlE+pAQAA0CpKDQsblZ0dluMSKAAAAIDYQKHhYZX84T0fOWHKggAAAJGAUgN+CBQAAABAbKDQ8LBK/mh6PsL1AjcAAIBIYJlSo7y8XFlZWfr8888D3j558mQtX77c9+eNGzcqOztbaWlpWrRokd99X375ZZ1zzjnq37+/XnzxxRDuOroQKAAAABAtyBdto9DwsEr+sML5AAAAiBSWKDXKy8t1zTXXtBo4Vq5cqfXr1/v+XFZWpmnTpmn69OnavHmzVq5cqQ0bNkiStm/frpkzZ2ru3Llav3695s2bp127dpkxRkQjUAAAACBakC/aZoVfoJM/DFY4HwAAAJHEEqXGTTfdpBkzZgS8raKiQvfff7/OP/9839rKlSvVv39/zZ07V0OGDNG8efP07LPPSpKWLl2qiRMnavbs2Ro+fLh+9KMfacWKFabMEakIFAAAAIgm5IvWWeEX6OQPgxXOBwAAQKSJC/cGJGnJkiXKysrSvffe2+K2+++/X9/85jdVU1PjW9u6dasmTpwom80mSRozZowefPBB321Tpkzx3XfMmDH6xS9+0eqxa2trVVtb6/uz0+mUJDU0NKihoUGSZLfbZbfb5XK55HK5fPf1rjc2Nsrtdp923eFwyGaz+R636bokNTY2+v5rs9nkdrvldrvV2OSYkhTncLRYt9lsctjtcrndfnv0rbtccjXdo80mu92uLw4f1qq1a5WekqKbJk1SfJznW6LR5Wo5k83WYt1ht3tmOrX3puvexzndel19vf6yfr1KKyo0Y8oU9U1NVcOpv4P2ztTocvm+H8w6T6dbj4uL85ynJus2m00Oh6PFHltbN/t7j5mYiZmYiZmYiZmYqSMzNb2flYQzX0jWyxjefLGlqEhbiop0+ejRumTkSL/n8MHMGK2tN7pcOlhaqhfXrVN6SoqmT56sxISEkGSMtmZqaGjQyrVrVXb0qC9/NLpcnZ6ps7kp7+OP9c6WLQHPh/drGxsb/c5tpPxs8O69PevMxEzMxEzMxEzMxExe7c0Xlig1srKyAq5v2LBBb731lv7zn//o7rvv9q07nU4NGzbM9+ekpCSVlJT4bmv6eE1vC+TRRx/V/PnzW6wXFhaqe/fukqQ+ffpo8ODBKi4uVllZme8+mZmZyszM1O7du1VZWelbHzRokNLT07V9+3a/sDR06FAlJyersLDQ75tnxIgRSkhIUH5+viRPCBo4cKBcbrdq6uq0bd8+330ddrtyzz1XldXV2nnwoG+9a0KCRg4cqHKnU3sPH/at9+rWTdmZmSqpqNCBigrfep+kJHWx27Xm/fc1aNAgZaSna9v+/cpMSVFmWpp2l5SosrramKlvX6X36qXt+/erpq7OmCkjQ8ndu6uwuNgvLIw45xwlxMUpf88ev7/XnMGDVdfQ4JvJ5XLpwOHDKq2o0PVXXqkSp1Mlp0JfR2aqratTamqqJJl2nnwz5eSorq5O27ZtM86Tw6Hc3FxVVlZq586dxnnq2lUjR45UeXm59u7da8zUq5eys7NVUlKiAwcOGOfJ5O89ZmImZmImZmImZmKmjszUPChZRTjzhWS9jOHNFxUnT+ry0aOVe8EFfs/Tg5kxBvfrp+LSUpWdek4vyZcxtnz6qXYfOODLH1UnT6prYmLQM0ZbMyU4HPp4+3adqK3VxaNH+/JHZ2fqbG6qqKxU+bFjumz0aI278MIWM2UmJys+Pl579uzx/buOpJ8N0fjzjpmYiZmYiZmYiZlCP9PAgQPVHja3hV5eZbPZVFxcrIEDB+rkyZMaMWKEFi9erKlTp+rWW2/V5ZdfrltvvVU33nijxo0bp3vuuUeSp3Xq0qWL6uvrdfHFF+uhhx7StGnTJEl79uzR5MmT9emnnwY8ZqBXUQ0YMEBHjhxRUlKSJPNbtUOHDmnZsmWa/Y1v6Ky0tJBcqfFleblWrlnju0IjIT7es/cgvOLIuy61/Soq7xUaZUeP6uarr1b/Pn06PdOhI0e07NVXdfvtt6tv3760n8zETMzETMzETMzETCbM5HQ6lZycrMrKSt9zZysJR76QrJcxvPli1NChmjp+vGc9BBmjrasaviwv15/XrFGf3r19+SMUGaOtmZpeIX7z1VerX1raGc3U2dyU9/HH2lhQoMtGjfK95VTzvZdWVGjJK69o1qxZ6tevnzFThPxskKLv5x0zMRMzMRMzMRMzhX6mEydOtCtfWOJKjUAeeeQR5ebmaurUqS1uS0lJ8WuCqqqqlHDqfVjbui2QxMREJSYmtliPi4tTXJz/X4/3JDTn/UZp73rzx22+7jj15Ntms8lmsykuwOO0tm632WQPtG63+z5A5WBpqa/QaO09bB0B5mxrPdBe2lpvbGz0BYqm72Hb2ZkcdrvvH4ZZ56k96zabLeB6a3vs6DozMVNr68zETBIztbbHjq4zEzNJzNTWHiOFWflCsl7G8OaL0dnZvnMW7IzR1vrpPkMjWBmjrZlq6+oC5o/OznS6vbe2/t7WrXpny5YWn6HRfO/e8+RwOFqc20j42dDRdWZiJomZWttjR9eZiZkkZmptjx1dZybzZ2pvvrBsqbFq1SqVlZUpOTlZklRdXa2XXnpJH374oXJzc7Vq1SrffQsLC5WRkSFJys3N1ebNmzVr1qwWt4EP5QMAAEBsIl+EB/nDwIeCAwAABIdlS43/+7//87v85Cc/+YnGjh2rW2+9VZL0wx/+UG+++aYuu+wyLVy4UJMmTZIk3XDDDRo3bpzuvfdeZWVl6amnntLNN98cjhEsh0ABAACAWEW+MB/5w0ChAQAAEDyWLTUyMzP9/tyjRw+lpaUp7dT7ni5evFhXX321evTooeTkZC1fvlySNHLkSN17773KyclRly5dNGTIEP3gBz8we/uWQ6AAAABALCNfmIv8YaDQAAAACC5LlRptfWa5N1R43XnnnZo0aZJ27typCRMmqEePHr7bfvWrX2nmzJk6ePCgLrvsstO+5220I1AAAAAgFpEvwoP8YaDQAAAACD5LlRodlZWVpaysrIC3DRs2TMOGDTN5R9ZDoAAAAADah3xx5sgfBgoNAACA0Gj5EeWIGgQKAAAAAGYhfxgoNAAAAEKHUiNKESgAAAAAmIX8YaDQAAAACC1KjShEoAAAAABgFvKHgUIDAAAg9Cg1ogyBAgAAAIBZyB8GCg0AAABzUGpEEQIFAAAAALOQPwwUGgAAAOah1IgSBAoAAAAAZiF/GCg0AAAAzEWpEQUIFAAAAADMQv4wUGgAAACYj1IjwhEoAAAAAJiF/GGg0AAAAAgPSo0IRqAwFBQVheW4AAAAQKwgfxgoNAAAAMKHUiNCESgM7xYUKJ9SAwAAAAgZ8oeBQgMAACC8KDUiEIHC4A0UOdnZYTk+AAAAEO3IHwYKDQAAgPCj1IgwBApD00AxilIDAAAACDryh4FCAwAAwBooNSIIgcJAoAAAAABCi/xhIH8AAABYB6VGhCBQGAgUAAAAQGiRPwzkDwAAAGuh1IgABAoDgQIAAAAILfKHgfwBAABgPZQaFkegMBAoAAAAgNAifxjIHwAAANYUF+4NoHWlFRVam5dHoBCBAgAAAAg1Cg0D+QMAAMC6uFLDwlZv2kSgEIECAAAACDUKDYMV8kdpRUVYjgsAABAJKDUsLCUpiUBhgUABAAAARDMKDYMV8sfB0lKt3rQpLMcGAACIBJQaFjZl3DgCBYUGAAAAEDIUGgYr5A/v+UhJSgrL8QEAACIBpYaFJcTHm35MAgUAAAAQGyg0DFbIH03Px5Rx48KyBwAAgEhAqQEfAgUAAAAQGyg0DFbIH83PRzhe4AYAABApKDUgiUABAAAAxAoKDYMV8ocVzgcAAEAkodQAgQIAAACIEVb4BTr5w2CF8wEAABBp4sK9AYQXgQIAAACIDaUVFVqbl0ehIWvkDwoNAACAzuFKjRhGoAAAAABix+pNmyg0ZI38QaEBAADQeZQaMYpAAQAAAMSWlKQkCg0L5A8KDQAAgDNDqRGDCBQAAABA7JkybhyFBoUGAABAxKPUiDEECgAAACA2JcTHm35M8oeBQgMAACA4KDViCIECAAAAgFnIHwYKDQAAgOCh1IgRBAoAAAAAZiF/GCg0AAAAgotSIwYQKAAAAACYhfxhoNAAAAAIPkqNKEegAAAAAGAW8oeBQgMAACA0KDWiGIECAAAAgFnIHwYKDQAAgNCh1IhSBAoAAAAAZiF/GCg0AAAAQotSIwoRKAAAAACYhfxhoNAAAAAIPUqNKEOgAAAAAGAW8oeBQgMAAMAclBpRhEABAAAAwCzkDwOFBgAAgHkoNaIEgQIAAACAWcgfBgoNAAAAc1FqRAECBQAAAACzkD8MFBoAAADmo9SIcAQKAAAAAGYhfxgoNAAAAMKDUiOCESgMpRUVYTkuAAAAECvIHwYKDQAAgPCh1IhQBArDwdJSrd60KSzHBgAAAGIB+cNAoQEAABBelBoRiEBh8AaKlKSksBwfAAAAiHbkDwOFBgAAQPhRakQYAoWhaaCYMm5cWPYAAAAARDPyh4FCAwAAwBooNSIIgcLQPFAkxMeHZR8AAABAtCJ/GCg0AAAArINSI0IQKAwECgAAACC0yB8G8gcAAIC1UGpEAAKFgUABAAAAhBb5w0D+AAAAsB5KDYsjUBgIFAAAAEBokT8M5A8AAABrotSwsLr6egLFKQQKAAAAILQoNAzkDwAAAOuKC/cG0Lq1eXk6VlVFoCBQAAAAACFFoWGwQv6oq683/ZgAAACRgis1LKzC6SRQWCBQAAAAANGMQsNghfxRW1entXl5ph8XAAAgUlBqWNjU8eMJFBQaAAAAQMhQaBiskD+856PC6TT92AAAAJGCUsPC0lNSwnJcAgUAAAAQ/Sg0DFbIH03Px9Tx400/PgAAQKTgMzXgh0ABAAAARD8KDYMV8kfz82G38/pDAACA1vBMCT4ECgAAACD6UWgYrJA/rHI+AAAAIgWlBiQRKAAAAIBYYJVfoJM/PKxyPgAAACIJpQYIFAAAAEAMqKuvt8Qv0MkfHhQaAAAAncNnasQ4AgUAAAAQG9bm5elYVRWFhgXyB4UGAABA53GlRgwjUAAAAACxo8LppNCwQP6g0AAAADgzlBoxikABAAAAxJap48dTaFBoAAAARDxKjRhEoAAAAABiT3pKSliOS/7woNAAAAAIDkqNGEOgAAAAAGAW8ocHhQYAAEDwUGrEEAIFAAAAALOQPzwoNAAAAIKLUiNGECgAAAAAmIX84UGhAQAAEHyUGjGAQAEAAADALOQPDwoNAACA0KDUiHIECgAAAABmIX94UGgAAACEDqVGFCNQAAAAADAL+cODQgMAACC0KDWiFIECAAAAgFnIHx4UGgAAAKFHqRGFCBQAAAAAzEL+8KDQAAAAMAelRpQhUAAAAAAwC/nDg0IDAADAPJQaUYRAAQAAAMAs5A8PCg0AAABzUWpECQIFAAAAALOQPzwoNAAAAMxnmVKjvLxcWVlZ+vzzz31rr776qgYNGqS4uDhdeOGFKioq8t22fft25ebmqnfv3pozZ47cbrfvto0bNyo7O1tpaWlatGiRmWOEBYECAAAA8Ee+CB3yhweFBgAAQHhYotQoLy/XNddc4xc49uzZo9tuu02PPfaYDh48qPPOO0+zZ8+WJNXW1uraa6/V6NGjlZ+frx07dmj58uWSpLKyMk2bNk3Tp0/X5s2btXLlSm3YsCEMU5mDQOFRV19v+jEBAABgTeSL0CF/eFBoAAAAhI8lSo2bbrpJM2bM8FsrKirSY489pu985zvq27ev7rrrLhUWFkqS1q5dq8rKSi1atEiDBw/WggUL9Oyzz0qSVq5cqf79+2vu3LkaMmSI5s2b57st2hAoPGrr6rQ2L8/04wIAAMCayBehQf7woNAAAAAIr7hwb0CSlixZoqysLN17772+tWuuucbvPrt27dKQIUMkSVu3btXYsWPVrVs3SdKIESO0Y8cO320TJ06UzWaTJI0ZM0YPPvhgq8eura1VbW2t789Op1OS1NDQoIaGBkmS3W6X3W6Xy+WSy+Xy3de73tjY6Hd5emvrDodDNpvN97hN1yWpsbHR91+bzSa32y23263GJseUpDiHQxu3bNHGggJdPnq0Lhk5Uo0ulxx2u1xut98ebTabZ93lkqvpHm02Y6YA640uV8uZbDa/9ZKyMq1au1bpKSm6adIkORwONZyawWH39GXN997aepzD0WJW397bmOlkXZ3+sn69jlZV+c65WefpdOtxcXGemZqs22w2ORyOFntsbd3s7z1mYiZmYiZmYiZmYqaOzNT0flYSznwhWS9jePNF0+fyHX0+/n8FBb78Me7CCyUpJBlD8mQGm83myxZeh8rL9ec1a1rkj2BnjLZmqquv11/Wr/cVGv3S0vz22dGZWtu77zw1Nvqd20j52eDde3vWmYmZmImZmImZmImZvNqbLyxRamRlZbV5e11dnZ544gndd999kjyhoOnXeP+Sjx49KqfTqWHDhvluS0pKUklJSauP/eijj2r+/Pkt1gsLC9W9e3dJUp8+fTR48GAVFxerrKzMd5/MzExlZmZq9+7dqqys9K0PGjRI6enp2r59u2pqanzrQ4cOVXJysgoLC/2+eUaMGKGEhATl5+dL8oSggQMHyuV2q6auTtv27fPd12G3q8bp1Ac7dijnwguV2LOn8vfsUdeEBI0cOFDlTqf2Hj7su3+vbt2UnZmpkooKHaio8K33SUrS4H79VFxaqrJTIUuSMlNSlJmWpt0lJaqsrjZm6ttX6b16afv+/aqpq9PJ2lodKC3VWenpuunrX9e2/fv9noiPOOccJcTFKX/PHr+/15zBg1XX0NBiptxzz1VldbV2HjzoWz/dTPvKypS/a5cSevTQ6AED9Omptxcw6zz5ZsrJUV1dnbZt22bM5HAoNzdXlZWV2rlzpzFT164aOXKkysvLtXfvXmOmXr2UnZ2tkpISHThwwDhPJn/vMRMzMRMzMRMzMRMzdWSm5kHJKsKZLyTrZQxvvthTWqp+qakdfj6+YcsWHTx61Jc/dpeUhCRj+GbKyFBy9+4qLC72ZYyTtbXK37pV6SkpGnr++dq2f7/v/sHMGG3NdPjYMR0sLVVCjx66evhwZaSnq+jAgU7PJLWemzKTkxUfH689e/b4/l1H0s+GaPx5x0zMxEzMxEzMxEyhn2ngwIFqD5vbQi+vstlsKi4ubrH5n/70p1q7dq0++ugjxcfH64EHHlB9fb3fh/QNGDBA77//vu677z6NGzdO99xzjyRPI9WlSxfVt/KZC4FeRTVgwAAdOXJESUlJksxv1Q4dOqRly5Zp9je+obPS0vye9OZ9/LHe2bLF7xVS3r87s67UOFhaqhfXrVOf3r01Y/JkdUlMbPcrjoL1KqqGhgatXLtWZUePavrkybLb7Vr26qu6/fbb1bdvX9pPZmImZmImZmImZmImE2ZyOp1KTk5WZWWl77mzlYQjX0jWyxjefPG9665TRp8+nvV2Ph/P+/hjbcjP98sfobwaXGp5VUNJWZlf/oiL839tnhlXatScPKkX16/35Y/M9PQzmul0ey+tqNCSV17RrFmz1K9fP2OmCPnZIEXfzztmYiZmYiZmYiZmCv1MJ06caFe+sMSVGm15++239fTTT+v9999XfHy8JCklJUXbt2/3u19VVZUSEhKUkpLi1xJ511uTmJioxMTEFutxcXEtnix7T0Jz3m+U9q43f9zm645TT75tNptsNpviTj3OuwUFemfLljbfw9Zus8ke4Lh2uz3gB6i0tu4IMKfkueTb+5ZTTd/DNq61WTuw3nRWvz02mynQe9h+WV7u+4dh1nlqz7rNZgu43toeO7rOTMzU2jozMZPETK3tsaPrzMRMEjO1tcdIE+p8IVkvY3jzhfcX61L7no+f7jM0gpUxWluPczh0sLQ0YP5o7f7NtTdjtLX32ro6vdjkLaeafoZGZ2Zqz7r3PDkcjhbnNhJ+NnR0nZmYSWKm1vbY0XVmYiaJmVrbY0fXmcn8mdqbLyzxQeGtKS4u1vTp0/X000/7XfKdm5urzZs3+92vtrZWKSkpLW4rLCxURkaGqfsOBT6Uz4MP5QMAAEBnkS/aj/zhQf4AAACwHsuWGjU1Nbrmmmt03XXX6Zvf/KaOHz+u48ePy+1269JLL5XT6dRzzz0nSVqwYIGuvPJKORwOTZs2TXl5eXrzzTdVX1+vhQsXatKkSWGe5swQKDwIFAAAAOgs8kX7kT88yB8AAADWZNm3n3r99de1Y8cO7dixQ0uWLPGte98Td+nSpZo+fbrmzJkju92ud955R5KUlpamxYsX6+qrr1aPHj2UnJys5cuXh2eIICBQeBAoAAAAcCbIF+1D/vAgfwAAAFiXpUqNph8Wct111/n9ublp06Zpz5492rJli8aOHavU1FTfbXfeeacmTZqknTt3asKECerRo0dI9x0qBUVFyi8qIlAQKAAAANAJ5IuOodDwIH8AAABYm6VKjY7q16+fpk6dGvC2rKwsZWVlmbyj4KLQIFAAAADAPNGeL9pCoeFhlfxRUFQUluMCAABEAst+pgaknOxsAoUFAgUAAAAQzSg0PKySP94tKFA+pQYAAECrKDUsbFR2dliOS6AAAAAAYgOFhodV8of3fOSEKQsCAABEAkoN+CFQAAAAALGBQsPDKvmj6fkI1wvcAAAAIgGlBnwIFAAAAEBsoNDwsEr+sML5AAAAiBSUGpBEoAAAAABihRV+gU7+MFjhfAAAAEQSSg0QKAAAAIAYYYVfoJM/DFY4HwAAAJEmLtwbQHgRKAAAAIDYUFBUpPyiIgoNi+QPCg0AAIDO4UqNGEagAAAAAGIHhYZ18geFBgAAQOdRasQoAgUAAAAQW3Kysyk0LJA/KDQAAADODKVGDCJQAAAAALFnVHZ2WI5L/jBQaAAAAJw5So0YQ6AAAAAAYBbyh4FCAwAAIDgoNWIIgQIAAACAWcgfBgoNAACA4KHUiBEECgAAAABmIX8YKDQAAACCi1IjBhAoAAAAAJiF/GGg0AAAAAg+So0oR6AAAAAAYBbyh4FCAwAAIDQoNaIYgQIAAACAWcgfBgoNAACA0KHUiFIECgAAAABmIX8YKDQAAABCi1IjChEoAAAAAJiF/GGg0AAAAAg9So0oQ6AAAAAAYBbyh4FCAwAAwByUGlGEQAEAAADALOQPA4UGAACAeSg1ogSBAgAAAIBZyB8GCg0AAABzUWpEAQIFAAAAALOQPwwUGgAAAOaj1IhwBAoAAAAAZiF/GCg0AAAAwoNSI4IRKAwFRUVhOS4AAAAQK8gfBgoNAACA8KHUiFAECsO7BQXKp9QAAAAAQob8YaDQAAAACC9KjQhEoDB4A0VOdnZYjg8AAABEO/KHgUIDAAAg/Cg1IgyBwtA0UIyi1AAAAACCjvxhoNAAAACwBkqNCEKgMBAoAAAAgNAifxjIHwAAANZBqREhCBQGAgUAAAAQWuQPA/kDAADAWig1IgCBwkCgAAAAAEKL/GEgfwAAAFgPpYbFESgMBAoAAAAgtMgfBvIHAACANcWFewNoXWlFhdbm5REoRKAAAAAAQo1Cw0D+AAAAsC6u1LCw1Zs2EShEoAAAAABCjULDYIX8UVpREZbjAgAARAJKDQtLSUoiUFggUAAAAADRjELDYIX8cbC0VKs3bQrLsQEAACIBpYaFTRk3jkBBoQEAAACEDIWGwQr5w3s+UpKSwnJ8AACASECpYWEJ8fGmH5NAAQAAAMQGCg2DFfJH0/MxZdy4sOwBAAAgElBqwIdAAQAAAMQGCg2DFfJH8/MRjhe4AQAARApKDUgiUAAAAACxgkLDYIX8YYXzAQAAEEkoNUCgAAAAAGKEFX6BTv4wWOF8AAAARJq4cG8A4UWgAAAAAGJDaUWF1ublUWjIGvmDQgMAAKBzuFIjhhEoAAAAgNixetMmCg1ZI39QaAAAAHQepUaMIlAAAAAAsSUlKYlCwwL5g0IDAADgzFBqxCACBQAAABB7powbR6FBoQEAABDxKDViDIECAAAAiE0J8fGmH5P8YaDQAAAACA5KjRhCoAAAAABgFvKHgUIDAAAgeCg1YgSBAgAAAIBZyB8GCg0AAIDgotSIAQQKAAAAAGYhfxgoNAAAAIKPUiPKESgAAAAAmIX8YaDQAAAACA1KjShGoAAAAABgFvKHgUIDAAAgdCg1ohSBAgAAAIBZyB8GCg0AAIDQotSIQgQKAAAAAGYhfxgoNAAAAEKPUiPKECgAAAAAmIX8YaDQAAAAMAelRhQhUAAAAAAwC/nDQKEBAABgHkqNKEGgAAAAAGAW8oeBQgMAAMBclBpRgEABAAAAwCzkDwOFBgAAgPkoNSIcgQIAAACAWcgfBgoNAACA8KDUiGAECkNpRUVYjgsAAADECvKHgUIDAAAgfCg1IhSBwnCwtFSrN20Ky7EBAACAWED+MFBoAAAAhBelRgQiUBi8gSIlKSksxwcAAACiHfnDQKEBAAAQfpQaEYZAYWgaKKaMGxeWPQAAAADRjPxhoNAAAACwBkqNCEKgMDQPFAnx8WHZBwAAABCtyB8GCg0AAADroNSIEAQKA4ECAAAACC3yh4H8AQAAYC2UGhGAQGEgUAAAAAChRf4wkD8AAACsh1LD4ggUBgIFAAAAEFrkDwP5AwAAwJooNSysrr6eQHEKgQIAAAAILQoNA/kDAADAuuLCvQG0bm1eno5VVREoCBQAAABASFFoGKyQP+rq600/JgAAQKTgSg0Lq3A6CRQWCBQAAABANKPQMFghf9TW1WltXp7pxwUAAIgUlBoWNnX8eAIFhQYAAAAQMhQaBivkD+/5qHA6TT82AABApKDUsLD0lJSwHJdAAQAAAEQ/Cg2DFfJH0/Mxdfx4048PAAAQKfhMDfghUAAAAADRj0LDYIX80fx82O28/hAAAKA1PFOCD4ECAAAAiH4UGgYr5A+rnA8AAIBIQakBSQQKAAAAIBZY5Rfo5A8Pq5wPAACASEKpAQIFAAAAEAPq6ust8Qt08ocHhQYAAEDnWKbUKC8vV1ZWlj7//HPf2vbt25Wbm6vevXtrzpw5crvdvts2btyo7OxspaWladGiRX6P9fLLL+ucc85R//799eKLL5o1QkQiUAAAACAakS9aWpuXF/ZfoJM/PCg0AAAAOs8SpUZ5ebmuueYav8BRW1ura6+9VqNHj1Z+fr527Nih5cuXS5LKyso0bdo0TZ8+XZs3b9bKlSu1YcMGSZ6gMnPmTM2dO1fr16/XvHnztGvXrjBMZX0ECgAAAEQj8kVgFU4nhYYF8geFBgAAwJmxRKlx0003acaMGX5ra9euVWVlpRYtWqTBgwdrwYIFevbZZyVJK1euVP/+/TV37lwNGTJE8+bN8922dOlSTZw4UbNnz9bw4cP1ox/9SCtWrDB9JqsjUAAAACBakS8Cmzp+PIUGhQYAAEDEiwv3BiRpyZIlysrK0r333utb27p1q8aOHatu3bpJkkaMGKEdO3b4bps4caJsNpskacyYMXrwwQd9t02ZMsX3OGPGjNEvfvGLVo9dW1ur2tpa35+dTqckqaGhQQ0NDZIku90uu90ul8sll8vlu693vbGx0e/S9dbWHQ6HbDab73GbrktSY2Oj7782m01ut1tut1uNTY4pSXEOR4t1m80mh90ul9vtt0ffussl16m95H38sTYWFGhiTo7GX3ihGk4dV5LsNptn7y5Xy5lsthbrDrvdM1OTx/CuS2qxd+/6F4cP68V165SekqKbJk1SYkLCGc3U6HL5vh/MOk+nW4+Li/PM1GTdZrPJ4XC02GNr62Z/7zETMzETMzETMzETM3Vkpqb3s5Jw5gvJehnDmy9Sk5N9XxvsjCEZWaL5et7HH+udLVt0+ejRumTkSF92CHbGaGumkrIyX/6YOWWK4uPj/R6/ozN1Jjd5P9Ok7OhRzZgyRX1TU1s9H771xka/cxspPxu8e2/POjMxEzMxEzMxEzMxk1d784UlSo2srKwWa06n02/d+xd59OhROZ1ODRs2zHdbUlKSSkpKAn5d09sCefTRRzV//vwW64WFherevbskqU+fPho8eLCKi4tVVlbmu09mZqYyMzO1e/duVVZW+tYHDRqk9PR0bd++XTU1Nb71oUOHKjk5WYWFhX7fPCNGjFBCQoLy8/MleULQwIED5XK7VVNXp2379vnu67DblXvuuaqsrtbOgwd9610TEjRy4ECVO53ae/iwb71Xt27KzsxUSUWFDlRUqKKyUuXHjmnsRRfp0lGjtOfQIZWdClmSlJmSosy0NO0uKVFldbUxU9++Su/VS9v371dNXZ0xU0aGkrt3V2Fxsd8T8RHnnKOEuDjl79nj9/eaM3iw9h8+rPeKijRo0CBlpKfrPwcOnNFMkucVT6mpqZJk2nnyzZSTo7q6Om3bts04Tw6HcnNzVVlZqZ07dxozde2qkSNHqry8XHv37jVm6tVL2dnZKikp0YEDB3zrZn/vMRMzMRMzMRMzMRMzdWSm5kHJKsKZLyTrZQxvvthTWqp+qamqa2gIasbw6pOUpMH9+qm4tNSXMSoqK/VpcbEm5uSoT3q6Xz4IZsZoa6aP9+7VgdJSDRo0SIMzMpSYkKDSyspOzyR1PDcNSk/XvzduVHy3brp40CCVOJ0qcTpbnSkzOVnx8fHas2eP7991JP1siMafd8zETMzETMzETMwU+pkGDhyo9rC5LfTyKpvNpuLiYg0cOFAPPPCA6uvr/T6kb8CAAXr//fd13333ady4cbrnnnskeVqnLl26qL6+XhdffLEeeughTZs2TZK0Z88eTZ48WZ9++mnAYwZ6FdWAAQN05MgRJSUlSTK/VTt06JCWLVum2d/4hs5KSwvaq6j+r7BQGwsKdNmoUZpw0UVBe8VRR15Fdai8XH9es8Z3hUZCfPwZzeTd+6EjR7Ts1Vd1++23q2/fvrSfzMRMzMRMzMRMzMRMJszkdDqVnJysyspK33NnKwlHvpCslzG8+eJ7112njD59POtByhhtXdXgvUL8slGjdNno0SG/GjzQTAcOH9bKtWvVp3dv3xXiwbj6pCO5qa6+Xn9Zv16lFRWaMWWK+p86B23tvbSiQkteeUWzZs1Sv379jJki5GeDFH0/75iJmZiJmZiJmZgp9DOdOHGiXfnCEldqBJKSkqLt27f7rVVVVSkhIUEpKSl+TZB33ft1rd0WSGJiohITE1usx8XFKS7O/6/HexKa836jtHe9+eM2X3ecChQ2m002m01xAR6ntXW7zSZ7gPVNpy75bv4etna7PeAHqzgCzNnWeqC9NF8/WFrqKzQCvYdtR2dquneH3e77h2HWeWrPus1mC7je2h47us5MzNTaOjMxk8RMre2xo+vMxEwSM7W1x0hhVr6QrJcxvPnCWxZIgZ+/n8nz8ebrmwoKWuSPUGSMttbbyh+dmakzuam2rs5XaLT1GRrN9+49Tw6Ho8W5jYSfDR1dZyZmkpiptT12dJ2ZmEliptb22NF1ZjJ/pvbmC0t8UHggubm52rx5s+/PxcXFqq2tVUpKSovbCgsLlZGREfDrmt4Wq/hQPgAAAMQ68oV5yB8efCg4AABAaFi21Lj00kvldDr13HPPSZIWLFigK6+8Ug6HQ9OmTVNeXp7efPNN1dfXa+HChZo0aZIk6YYbbtBf/vIXffLJJzp+/Lieeuop322xiEABAAAAkC/MQv7woNAAAAAIHcu+/VRcXJyWLl2q6dOna86cObLb7XrnnXckSWlpaVq8eLGuvvpq9ejRQ8nJyVq+fLkkaeTIkbr33nuVk5OjLl26aMiQIfrBD34QvkHCiEABAAAAeJAvQo/84UGhAQAAEFqWKjWaf2b5tGnTtGfPHm3ZskVjx45Vamqq77Y777xTkyZN0s6dOzVhwgT16NHDd9uvfvUrzZw5UwcPHtRll1122ve8jUYECgAAAMQ68oV5yB8eFBoAAAChZ6lSI5B+/fpp6tSpAW/LyspSVlZWwNuGDRumYcOGhXJrlkWgAAAAAAIjXwQf+cODQgMAAMAclv1MDXQOgQIAAACAWcgfHhQaAAAA5qHUiCIECgAAAABmIX94UGgAAACYi1IjShAoAAAAAJiF/OFBoQEAAGA+So0oQKAAAAAAYBbyhweFBgAAQHhQakQ4AoVHXX296ccEAAAAYg35w4NCAwAAIHwoNSIYgcKjtq5Oa/PyTD8uAAAAEEvIHx4UGgAAAOFFqRGhCBQe3kBR4XSafmwAAAAgVpA/PCg0AAAAwo9SIwIRKDyaBoqp48ebfnwAAAAgFpA/PCg0AAAArCEu3BtAxxAoPJoHCrudfg4AAAAINvKHB4UGAACAdfCb4AhCoPAgUAAAAAChR/7wIH8AAABYC6VGhCBQeBAoAAAAgNAjf3iQPwAAAKyHUiMCECg8CBQAAABA6JE/PMgfAAAA1kSpYXEECg8CBQAAABB65A8P8gcAAIB18UHhFlZQVKT8oiICBYECAAAACDkKDQ/yBwAAgLVxpYaFUWgQKAAAAAAzUGh4WCV/FBQVheW4AAAAkYBSw8JysrMJFBYIFAAAAEA0o9DwsEr+eLegQPmUGgAAAK2i1LCwUdnZYTkugQIAAACIDRQaHlbJH97zkROmLAgAABAJKDXgh0ABAAAAxAYKDQ+r5I+m5yNcL3ADAACIBJQa8CFQAAAAALGBQsPDKvnDCucDAAAgUlBqQBKBAgAAAIgVVvgFOvnDYIXzAQAAEEkoNUCgAAAAAGKEFX6BTv4wWOF8AAAARJq4cG8A4UWgAAAAAGJDQVGR8ouKKDQskj8oNAAAADqHKzViGIECAAAAiB0UGtbJHxQaAAAAnUepEaMIFAAAAEBsycnOptCwQP6g0AAAADgzlBoxiEABAAAAxJ5R2dlhOS75w0ChAQAAcOYoNWIMgQIAAACAWcgfBgoNAACA4KDUiCEECgAAAABmIX8YKDQAAACCh1IjRhAoAAAAAJiF/GGg0AAAAAguSo0YQKAAAAAAYBbyh4FCAwAAIPgoNaIcgQIAAACAWcgfBgoNAACA0KDUiGIECgAAAABmIX8YKDQAAABCh1IjShEoAAAAAJiF/GGg0AAAAAgtSo0oRKAAAAAAYBbyh4FCAwAAIPQoNaIMgQIAAACAWcgfBgoNAAAAc1BqRBECBQAAAACzkD8MFBoAAADmodSIEgQKAAAAAGYhfxgoNAAAAMxFqREFCBQAAAAAzEL+MFBoAAAAmI9SI8IRKAAAAACYhfxhoNAAAAAID0qNCEagMBQUFYXluAAAAECsIH8YKDQAAADCh1IjQhEoDO8WFCifUgMAAAAIGfKHgUIDAAAgvCg1IhCBwuANFDnZ2WE5PgAAABDtyB8GCg0AAIDwo9SIMAQKQ9NAMYpSAwAAAAg68oeBQgMAAMAaKDUiCIHCQKAAAAAAQov8YSB/AAAAWAelRoQgUBgIFAAAAEBokT8M5A8AAABrodSIAAQKA4ECAAAACC3yh4H8AQAAYD2UGhZHoDAQKAAAAIDQIn8YyB8AAADWFBfuDaB1pRUVWpuXR6AQgQIAAAAINQoNA/kDAADAurhSw8JWb9pEoBCBAgAAAAg1Cg2DFfJHaUVFWI4LAAAQCSg1LCwlKYlAYYFAAQAAAEQzCg2DFfLHwdJSrd60KSzHBgAAiASUGhY2Zdw4AgWFBgAAABAyFBoGK+QP7/lISUoKy/EBAAAiAaWGhSXEx5t+TAIFAAAAEBsoNAxWyB9Nz8eUcePCsgcAAIBIQKkBHwIFAAAAEBsoNAxWyB/Nz0c4XuAGAAAQKSg1IIlAAQAAAMQKCg2DFfKHFc4HAABAJKHUAIECAAAAiBFW+AU6+cNghfMBAAAQaeLCvQGEF4ECAAAAiA2lFRVam5dHoSFr5A8KDQAAgM7hSo0YRqAAAAAAYsfqTZsoNGSN/EGhAQAA0HmUGjGKQAEAAADElpSkJAoNC+QPCg0AAIAzQ6kRgwgUAAAAQOyZMm4chQaFBgAAQMSj1IgxBAoAAAAgNiXEx5t+TPKHgUIDAAAgOCg1YgiBAgAAAIBZyB8GCg0AAIDgodSIEQQKAAAAAGYhfxgoNAAAAIKLUiMGECgAAAAAmIX8YaDQAAAACD5KjShHoAAAAABgFvKHgUIDAAAgNCg1ohiBAgAAAIBZyB8GCg0AAIDQodSIUgQKAAAAAGYhfxgoNAAAAEKLUiMKESgAAAAAmIX8YaDQAAAACL0zLjVqa2v1q1/9qs37rFu37kwPg3YiUAAAACCSkS8iC/nDQKEBAABgjjMuNWw2mxYsWNDmfWbMmHGmh0E7ECgAAAAQ6cgXkYP8YaDQAAAAME9cR79gxYoV6tq1qySprq5OM2bMUPfu3fXBBx/orbfeUlxcnBoaGjRt2jTNnj1bb7/9tpKSkoK+cfgjUAAAACASkS8iE/nDQKEBAABgrg6XGnfccYeSk5Nlt9vldDo1Y8YMJSUl6YMPPtALL7yg/fv3q2/fvho8eLAOHTqkLl26KD4+PhR7xykECgAAAEQq8kXkIX8YKDQAAADM1+G3nxo6dKh++tOfauHChRoyZIgkzyXikvTrX/9aGRkZuvvuuyVJCQkJstv5LPJQIlAAAAAgkpEvIgv5w0ChAQAAEB4kgghGoDCUVlSE5bgAAABArCB/GCg0AAAAwodSI0IRKAwHS0u1etOmsBwbAAAAiAXkDwOFBgAAQHgFtdT42c9+ppKSEj399NOaP3++Dh48qEsvvVROp7PTj7l06VINGDBA3bp10+WXX669e/dKkrZv367c3Fz17t1bc+bMkdvt9n3Nxo0blZ2drbS0NC1atOiM57IaAoXBGyhS+LBIAACAqBOKfCGRMTqK/GGg0AAAAAi/oJYaV1xxhXr06KGcnBxdeuml6tmzp2655RZ16dKlU4+3Z88e/eIXv9Crr76qnTt3avDgwbr11ltVW1ura6+9VqNHj1Z+fr527Nih5cuXS5LKyso0bdo0TZ8+XZs3b9bKlSu1YcOGIE4ZXgQKQ9NAMWXcuLDsAQAAAKET7HwhkTE6ivxhoNAAAACwhqCWGldeeaWSkpJ08cUXa+LEiUpKStKsWbOU0Mkne4WFhRo7dqxGjRqls88+W9/73vf02Wefae3ataqsrNSiRYs0ePBgLViwQM8++6wkaeXKlerfv7/mzp2rIUOGaN68eb7bIh2BwtA8UCTEx4dlHwAAAAidYOcLiYzREeQPA4UGAACAdVj6MzWGDRumt99+Wx9//LEqKyv1hz/8QV//+te1detWjR07Vt26dZMkjRgxQjt27JAkbd26VRMnTpTNZpMkjRkzRlu2bAnbDMFCoDAQKAAAANBZZIz2IX8YyB8AAADWEtfRLzhx4oSKi4tVUVGh6upqv9tKSkrU0NCgsrIyZWRknPHmhg0bpm9961u66KKLJElZWVn64IMP9NhjjykrK8t3P5vNJofDoaNHj8rpdGrYsGG+25KSklRSUtLqMWpra1VbW+v7s/f9eRsaGtTQ0CBJstvtstvtcrlccrlcvvt61xsbG/3eb7e1dYfDIZvN5nvcpuuS1NjY6PuvzWaT2+2W2+1WTW2t/rJ+vcqOHtWMKVOUkZ4ut9utxiZ7sdlsctjtcrndfnv0rbtccjXdo81mzBRgvdHlajHTpsJCvbNliy4fPVqXjByphsZGOex2z0yn9u6bye7py5rusa31OIejXTOVlJXpxXXrlJ6SoumTJ8vhcKihsVGNLpcvZJp1nk63HhcX55mpybr3e7X5HltbN/t7j5mYiZmYiZmYiZmYqSMzNb1fZ5mZL6TYzBjefNH0OX5bz8fr6ut9+eO/pk7VWX36+D3fD2bGsDfblyRfxti4ZYs2FhT48kdrez/TjNHWTF+Wl2vlmjVKT0nRTZMm+fJHZ2dqb27yzdrY6HduI+Vng3fv7VlnJmZiJmZiJmZiJmbyam++6HCpsXfvXt+l1t6/gOrqarndbv3whz+UJC1cuFCrVq3SyZMnVVlZ6ftlc0d9+OGHeu211/T+++9r6NChWrhwoa6++mpdccUVSkxM9Ltvly5dVF1drbi4OL/bvOutefTRRzV//vwW64WFherevbskqU+fPho8eLCKi4tVVlbmu09mZqYyMzO1e/duVVZW+tYHDRqk9PR0bd++XTU1Nb71oUOHKjk5WYWFhX7fPCNGjFBCQoLy8/MleULQwIED5XK7VXnihN4oKFBCjx66eNAgHT5+XGdLqqyu1s6DB32P0TUhQSMHDlS506m9hw/71nt166bszEyVVFToQEWFb71PUpIG9+un4tJSlTX5oMXMlBRlpqVpd0mJKpv8vZ1wOrWpoEAXX3SREnv2VP6ePZ6ZMjKU3L27CouL/Z6IjzjnHCXExfnu55UzeLDqGhq0bd8+35rDblfuueeedqaTtbU6UFqqswcM0PUTJqjM6fTNVFtXp9TUVEky7Tz5ZsrJUV1dnbZt22bM5HAoNzdXlZWV2rlzpzFT164aOXKkysvLfR9IKUm9evVSdna2SkpKdODAAeM8mfy9x0zMxEzMxEzMxEzM1JGZmgelzjAzX0ixmTG8+WJPaan6paa2+Xx8xxdf6GBpqRJ69NCE885TRnq6SisrQ5IxBvXtq/RevbR9/37V1NUZM2VkaNuuXSouL1fOhRf68kcoMkZbM52srdWuvXuVnpKicTk52rZ//xnN1JHclJmcrPj4eO3Zs8f37zqSfjZE4887ZmImZmImZmImZgr9TAMHDlR72NxBeHlVr169VFJSouPHjyshIUEnT55UcnKy5s+frwcffFBDhw7VoUOHOvy4P/7xj2W32/XEE09I8jQ1qampeuCBB7R9+3atWLHCd9/k5GR9+umnmjdvntLS0vTII49Iko4dO6aMjAydOHEi4DECvYpqwIABOnLkiJKSkiSZ36odOnRIy5Yt03enTtXbH32ksqNHNX3yZPXv00dScF5x1JFXUeV9/LHe2bJFE3NyNO7CC8/oFUedfRXVgcOH9eK6derTu7dumjRJ3bp08dv7oSNHtOzVV3X77berb9++tJ/MxEzMxEzMxEzMxEwmzOR0OpWcnKzKykrfc+dgCFW+kGIzY3jzxfeuu04ZpzJFoOfjJ2trtWrdOl/+yEhPD/nV4IGuavDmj8tHj9a4Cy80ZgpyxmhrJu8V4n1699bMKVMUHxcXlKtP2pubSisqtOSVVzRr1iz169fPmClCfjZI0ffzjpmYiZmYiZmYiZlCP9OJEyfalS86fKVGc9XV1aqtrVX37t19rzryeuyxxyR5GpzOcLlcKi8v9/25qqrK90qpzZs3+9aLi4tVW1urlJQU5ebmatWqVb7bCgsL27xUPTExscUrsiTPSY6L8//r8Z6E5rzfKO1db/64zdcdp558r3vvPR2rqgr4HrY2m01xAR7fbrPJHmjdbg/4ASqtrXufXL9bUOArNNp6D9tAe+noemszfVlWplVr17Z4D9ume3fY7b5/GGadp/as22y2gOut7bGj68zETK2tMxMzSczU2h47us5MzCQxU1t7DLZQ5gspNjOGN194f7EutXw+XltXp1Xr1gX8DI1gZ4y21tuTP4KRMdqa6cvS0sD54zR7b896e/defyrgOxyOFuc2En42dHSdmZhJYqbW9tjRdWZiJomZWttjR9eZyfyZ2psvzviDwhMTE/XGG2+0eZ9PPvmkU489YcIE/eMf/9DixYu1atUqfeMb31C/fv10zz33yOl06rnnnpMkLViwQFdeeaUcDoemTZumvLw8vfnmm6qvr9fChQs1adKkTh0/3CqcTj6Ujw/lAwAAiCmhzBcSGSMQPhTcYIX8UVtXp7V5eaYfFwAAIFKc8ZUaDodDEyZMCMZeWrjhhhtUVFSkJ598Ul9++aUuuOACvfLKK4qPj9fSpUs1ffp0zZkzR3a7Xe+8844kKS0tTYsXL9bVV1+tHj16KDk5WcuXLw/J/kJt6vjxBAoKDQAAgJgSynwhkTGao9AwWCF/eM9HRZPPJAEAAIC/Tl+pUVlZqTvuuKPF+14F8sQTT6iqqqrDx7DZbJo7d6727dunuro6FRQU6KKLLpIkTZs2TXv27NHzzz+voqIiDRs2zPd1d955p3bt2qWVK1dq27Zt6tu3b4ePbQXpKSlhOS6BAgAAAGYzI19IZIymKDQMVsgfTc/H1PHjTT8+AABApOj0lRoul0tLly7VoEGD1LdvX2VlZekrX/lKi/e3feONN/TAAw8oNTVVt95665nu10+/fv00derUgLdlZWUpKysrqMeLBQQKAAAAhIMV8oUUOxmDQsNghfzR/HwEeo9qAAAAeHS61PA+yXrnnXf05Zdf6ssvv9SRI0d07rnn6utf/7ruuusuOZ1O3XDDDfr2t78dksCB4CJQAAAAIFzIF+ah0DBYIX8EOh9fNvkwewAAAPg7o8/UsNlsWrdune/PR44cUV5env785z9rxIgRSkhI0MyZM/XMM8+c8UYRWgQKAAAAhBv5IvQoNAxWyB9WOR8AAACRpEOlxt69ezVjxgx9+9vf1tixY1vcXlVVpT179qiwsFD9+vVTjx49dPz4ccXFnfHnkSOECBQAAAAIB/KFuerq6y3xC3TyhweFBgAAQOd06I06q6qqdP755+u5557ThAkT5Ha7dcstt+gb3/iGzjnnHGVnZ2vdunWaN2+ePv/8c+Xl5enDDz/Uww8/HKr94wwRKAAAABAu5Atzrc3LC/sv0MkfHhQaAAAAndehlziNHDlSzz//vCRp165d+sc//qG33npL77zzjtLT0/Xyyy/7faheWlqaVq5cqYkTJ+rGG2/U8OHDg7t7nBECBQAAAMKJfGGuCqdTt1xzDYUGhQYAAEBE69CVGpL0ve99Tx999JHWr1+v9957T2+++aZWrFihr33ta1q/fr0WLVqk/fv3S5Lefvtt/fOf/9T06dP173//O+ibR+cRKAAAAGAF5AvzTB0/nkKDQgMAACDidehKjZqaGvXv31/XXHONrrrqKiUmJmr9+vW666679Ne//lUXXHCBzj77bP3sZz/Tt7/9be3fv18TJ07U448/rrS0tFDNgA4iUAAAAMAKyBfmSk9JCctxyR8eFBoAAADB0aErNeLj4zVv3jx99tlnuuSSS2S327V79269/PLLuuKKKyRJ3bp1U1FRkRoaGrRp0yaNGDGCwGEhBAoAAABYBfki+pE/PCg0AAAAgqdDV2r89re/1Zw5c2Sz2Xxrf//73yVJ//Vf/6WLLrpINptNWVlZ+t73vqeNGzfqjjvu0MUXX6yMjIzg7hwdRqAAAACAlZAvohv5w4NCAwAAILg6VGrMmDFDEyZMkM1m02uvvaZf/vKXGj58uJ555hnt3r1b3//+9+VyufTHP/5RxcXFuuuuu1RTU6Mf//jHeumll0I1A9qBQAEAAACrIV9EL/KHB4UGAABA8HWo1DjrrLO0e/du3XHHHfqv//ovfe1rX1N8fLweffRRvfrqqxo7dqyGDx+uZcuWqaCgQHl5ebrggguUmZmpnTt3aujQoaGaA20gUAAAAMCKyBfRifzhQaEBAAAQGh36TI3a2lrNnj1bDz/8sHr16qX4+Hj9/e9/1759+/Tzn/9cdrtdDodDH330kV555RWNGTNGPXr00PXXX68//elPoZoBbSBQAAAAwKrIF9GH/OFBoQEAABA6HbpSIzExUVu3blW3bt30wQcfKCUlRV27dtWzzz6rdevWqUuXLrr22mslSdOmTfN93e23366srKzg7hynRaAAAACAlZEvogv5w4NCAwAAILQ6VGpIUrdu3SRJF198sS6++GJJUk5OjnJyciQp4HvbfvWrXz2TPaITCBQAAACIBOSL6ED+8KDQAAAACL0Ovf1UU9XV1ae9T0NDg37xi1/o4MGDnT0MOoFAAQAAgEhDvohc5A8PCg0AAABzdKrUOHnypHr27Kn9+/e3eT+Hw6H58+fr2LFjnTkMOoFAAQAAgEhDvohc5A8PCg0AAADzdKrU6NKli9xutxITE7V582a99dZbamxsbHE/m80mt9ut+Pj4M94oTo9AAQAAgEhEvohM5A8PCg0AAABzdfrtp2w2m2w2m5YtW6avf/3rSklJ0bRp07R48WIVFBTI7XYHc584DQIFAAAAIhn5IrKQPzwoNAAAAMzX4Q8K9/KGijlz5ujyyy/XJ598ooKCAi1YsED333+/evbsqa9+9auy2WxB2ywCI1AAAAAg0pEvIgf5w4NCAwAAIDw6VGqUlJTopz/9qc4//3xfmDjvvPN03nnn+d3v008/1Xvvvac333wzeDtFQAQKj7r6etOPCQAAgDNDvog85A8PCg0AAIDwaffbTzU2Nio3N1cbNmxQ375927xv//79NWDAAK1YsYLLxEOIQOFRW1entXl5ph8XAAAAnUe+iDzkDw8KDQAAgPBq95UaDodDL774onJyctStWzfdfvvtfrdv375dr7zyitatW6ePPvpIjY2N+s9//hP0DcODQOHhDRQVTqfpxwYAAEDnkS8iC/nDg0IDAAAg/Dr0QeGXXnqpunXrJkm+y8M/+ugjnXvuuRo5cqT+93//V8OGDdNzzz2nffv2aejQocHfMQgUpzQNFFPHjzf9+AAAADgz5IvIQP7woNAAAACwhk59UPjJkyfldrt18uRJnX/++Zo2bZpuuukmjRkzpsV9bTabXC7XGW8UHgQKj+aBwm7vUD8HAAAACyFfWBf5w4NCAwAAwDo6VGo0NDQoLi5OdrtdixcvVnJyspKSkrRo0aKA96+trZXb7VZ1dXVQNhvrCBQegQLFl+Xlpu8DAAAAZ4Z8YW3kDw8KDQAAAGvpUKkxa9Ys1dTU6Pvf/77uuece3yXirUlMTFR9fb0cDscZbRIECi8CBQAAQPQgX1gX+cOD/AEAAGA97S41Ghsb5XK59NZbb+nvf/+7UlJS1Lt373Z/bW1trQ4cONDpjcYyAoUHgQIAACB6kC+si/zhQf4AAACwpnaXGg6HQytWrFBjY6PWrVunp59+Wq+//rokaebMmbriiisCfl1DQ4Pq6+tVWVkZnB3HGAKFB4ECAAAgupAvrIn84UH+AAAAsK4Of1C4w+HQ1KlTNXXqVO3atUtz587VihUrdOzYMf3+97/XgAEDQrHPmFRQVKT8oiICBYECAAAgapEvrINCw4P8AQAAYG32M/ni888/Xy+99JLWrVunzMxMJScnB2lbkEShIQIFAABALCFfhA+FhodV8kdBUVFYjgsAABAJOnylRiBXXXWVrrrqqmA8FJrIyc4mUFggUAAAAMBc5AtzUWh4WCV/vFtQoHxKDQAAgFZ1qNTYt2+fHn74YXXp0kU2m02/+93vVFdXp3PPPVeJiYmKi2v5cAkJCZo5c6bmzp0btE3HilHZ2WE5LoECAAAAZiBfhB+FhodV8of3fORkZ1NsAAAAtKJDpUZjY6MOHz6sxMRErVmzRr/5zW/kdrtVVlam3/3udwG/Zv369Zo/f75+9KMfqXfv3kHZNEKHQAEAAACzkC/Ci0LDwyr5o+n5GHL22ZQaAAAArehQqTFo0CC9/vrrkiS73fg4DpvNph/+8Id+9/3iiy9UW1urKVOm6N///rcKCgr0ta99LQhbRqgQKAAAAGAm8kX4UGh4WCV/ND8fX5aXh2UfAAAAkeCMPii8uTvuuEPHjx+XJP33f/+3fvKTn6h3796aNm2a+vXrF8xDIcgIFAAAALAa8kVoUGh4WCV/WOF8AAAARJKgfFC4JJWWlur111/X7bffrh/84Af661//qrVr16p379765z//GazDIAQIFAAAALAa8kVoWOEX6OQPgxXOBwAAQKTpUKlRW1urxMTEgLelp6fr7bff1sUXX6w1a9boO9/5jiZNmhSUTSJ0CBQAAAAIF/KFuQqKipRfVEShYZH8QaEBAADQOe1++6nKykp17dpVDodDDodDNputxX0GDRqkOXPm6Pjx4/rud78b1I0i+AgUAAAACBfyhfkoNKyTPyg0AAAAOq/dV2p069ZNzzzzjLp16yZJuuWWWwLeb8WKFTr77LP14IMPatKkSXI4HMHZKYKKQAEAAIBwIl+YLyc7m0LDAvmDQgMAAODMtPtKjfj4eN1xxx26+eabdfPNN8vtdre4z9///nd9+eWX+vDDD1VfX69nnnkmqJtFcBAoAAAAEG7kC/ONys4Oy3HJHwYKDQAAgDPX7lKjPZ577jl9//vfV58+fTR37lw98sgjqqmpCeYhcIYIFAAAAIgU5IvIR/4wUGgAAAAER4c+KPx0nn76acXHx0uSvvOd7+gPf/iDqqqq1LVr12AeBp1EoAAAAEAkIV9ENvKHgUIDAAAgeDpUanz66af629/+pi5dugT8IL9zzjnHeOC4OL377ruy24N6MQg6iUABAAAAqyFfRC/yh4FCAwAAILg6VGocPHhQCxcuVNeuXZWeni673a7Gxka53e7TfmBfY2PjGW0UnUegAAAAgBWRL6IT+cNAoQEAABB8HSo1Lr/8ch07dsxvrbGxUevWrVNCQoIcDodsNptsNpvcbrdcLpcaGhpUX18fzD2jAwgUAAAAsCryRfQhfxgoNAAAAELjjD9Tw+Fw6KqrrgrGXhBkBAoAAABEGvJF5CJ/GCg0AAAAQoc3pI1SBAoAAAAAZiF/GCg0AAAAQotSIwoRKAAAAACYhfxhoNAAAAAIPUqNKEOgAAAAAGAW8oeBQgMAAMAclBpRhEABAAAAwCzkDwOFBgAAgHkoNaIEgQIAAACAWcgfBgoNAAAAc1FqRAECBQAAAACzkD8MFBoAAADmo9SIcAQKAAAAAGYhfxgoNAAAAMKDUiOCESgMBUVFYTkuAAAAECvIHwYKDQAAgPCh1IhQBArDuwUFyqfUAAAAAEKG/GGg0AAAAAgvSo0IRKAweANFTnZ2WI4PAAAARDvyh4FCAwAAIPwoNSIMgcLQNFCMotQAAAAAgo78YaDQAAAAsAZKjQhCoDAQKAAAAIDQIn8YyB8AAADWQakRIQgUBgIFAAAAEFrkDwP5AwAAwFooNSIAgcJAoAAAAABCi/xhIH8AAABYD6WGxREoDAQKAAAAILTIHwbyBwAAgDXFhXsDaF1pRYXW5uURKESgAAAAAEKNQsNA/gAAALAurtSwsNWbNhEoRKAAAAAAQo1Cw2CF/FFaURGW4wIAAEQCSg0LS0lKIlBYIFAAAAAA0YxCw2CF/HGwtFSrN20Ky7EBAAAiAaWGhU0ZN45AQaEBAAAAhAyFhsEK+cN7PlKSksJyfAAAgEhAqWFhCfHxph+TQAEAAADEBgoNgxXyR9PzMWXcuLDsAQAAIBJQasCHQAEAAADEBgoNgxXyR/PzEY4XuAEAAEQKSg1IIlAAAAAAsYJCw2CF/GGF8wEAABBJKDVAoAAAAABihBV+gU7+MFjhfAAAAESauHBvAOFFoAAAAABiQ2lFhdbm5VFoyBr5g0IDAACgc7hSI4YRKAAAAIDYsXrTJgoNWSN/UGgAAAB0HqVGjCJQAAAAALElJSmJQsMC+YNCAwAA4MxQasQgAgUAAAAQe6aMG0ehQaEBAAAQ8SKm1HjggQd07bXX+v68fft25ebmqnfv3pozZ47cbrfvto0bNyo7O1tpaWlatGhROLZrWQQKAAAAwCPWMkZCfLzpxyR/GCg0AAAAgiMiSo1t27bpD3/4g377299Kkmpra3Xttddq9OjRys/P144dO7R8+XJJUllZmaZNm6bp06dr8+bNWrlypTZs2BDG3VsHgQIAAADwIGOEHvnDQKEBAAAQPJYvNVwul+644w79+Mc/1qBBgyRJa9euVWVlpRYtWqTBgwdrwYIFevbZZyVJK1euVP/+/TV37lwNGTJE8+bN890WywgUAAAAgAcZI/TIHwYKDQAAgOCyfKnxzDPP6JNPPtHAgQP1r3/9S3V1ddq6davGjh2rbt26SZJGjBihHTt2SJK2bt2qiRMnymazSZLGjBmjLVu2hG3/VkCgAAAAAAxkjNAifxgoNAAAAIIvLtwbaMvx48f1P//zPxo0aJD27dunFStW6Je//KUmTJigrKws3/1sNpscDoeOHj0qp9OpYcOG+W5LSkpSSUlJq8eora1VbW2t789Op1OS1NDQoIaGBkmS3W6X3W6Xy+WSy+Xy3de73tjY6Pd+u62tOxwO2Ww23+M2XZekxsZG339tNpvcbrfcbrcamxxTkuIcjhbrNptNDrtdLrfbb4919fX6y/r1Kq2o0IwpU9Q3NVUNjY2y22zGTE33fmq90eVqOZPN1mLdYbd7Zjq196brknx7zPv4Y20sKNDlo0drwkUXtbh/R2byrTfZe6PL5QuZZp2n063HxcV5Zmqy7v1ebb7H1tbN/t5jJmZiJmZiJmZiJmbqyExN7xcpYjFjePNF0+fyZ5IxAj0flzxZor6hQSvXrlXZ0aO+/OFyuUKSMdpaz/v4Y72zZYsuHz1al4wc6XuszszU2dx0sLRUL65bp/SUFM2YPFmJCQntnsl3nhob/c5tpPxs8O69PevMxEzMxEzMxEzMxExe7c0Xli41/vGPf+jEiRPasGGD0tLS1NDQoOHDh2vZsmW67bbb/O7bpUsXVVdXKy4uTomJiS3WW/Poo49q/vz5LdYLCwvVvXt3SVKfPn00ePBgFRcXq6yszHefzMxMZWZmavfu3aqsrPStDxo0SOnp6dq+fbtqamp860OHDlVycrIKCwv9vnlGjBihhIQE5efnS/KEoIEDB8rldqumrk7b9u3z3ddhtyv33HNVWV2tnQcP+ta7JiRo5MCBKnc6tffwYUmeX+7vO3hQpRUVuvqyy1TidKrkVKDqk5Skwf36qbi0VGWn1iQpMyVFmWlp2l1Sosomf2+D+vZVeq9e2r5/v2rq6oyZMjKU3L27CouL/Z6IjzjnHCXExSl/zx5VVFaq/Ngx5Vx4ocZdeOEZzSRJvbp1U3ZmpkoqKnSgosLzd1ZXp9TUVEky7Tx55eTkqK6uTtu2bTNmcjiUm5uryspK7dy505ipa1eNHDlS5eXl2rt3rzFTr17Kzs5WSUmJDhw44Fs3+3uPmZiJmZiJmZiJmZipIzM1D0qRIBYzhjdf7CktVb/UVNU1NAT9+bgk9e7eXXn5+aqXdPHo0b78EYqM0VTO4MF+M1VUVqq0okITc3I04vzz/e7f0Zk6m5s+2LVLe0tKNGjQIGWkp+tkQ4O6JCa2e6bM5GTFx8drz549vn/XkfSzIRp/3jETMzETMzETMzFT6GcaOHCg2sPmtvDLqxYsWKA333xTb7/9tm/txhtv1EsvvaSbb75ZK1as8K0nJyfr008/1bx585SWlqZHHnlEknTs2DFlZGToxIkTAY8R6FVUAwYM0JEjR5SUlCTJ/Fbt0KFDWrZsmWZ/4xs6Ky2tU6+i8l6hUXb0qG6++mqdlZYWtFccdeRVVO8WFGhjQYEuGzVK4y68sNVXIp3pK8MOHTmiZa++qttvv119+/al/WQmZmImZmImZmImZjJhJqfTqeTkZFVWVvqeO1tdLGYMb7743nXXKaNPH896kJ+PN71CfOap/OHbe5AzRltXanivEL9s1ChdNnp0UK4+6Whu+rKsTH9es0Z9evfWTZMmKSE+vsMzlVZUaMkrr2jWrFnq16+fbz1SfjZI0ffzjpmYiZmYiZmYiZlCP9OJEyfalS8sfaVGZmamX4MjSfv27dOTTz6p3/3ud7614uJi1dbWKiUlRbm5uVq1apXvtsLCQmVkZLR6jMTERL9XXXnFxcUpLs7/r8d7EprzfqO0d7354zZfd5wKFDabTTabTXEBHqe1dbvNpvrGRl+gaPoetoE+QMVutwdcdwSYs631QHt5t6BA72zZEvA9bDs6kz3QepO9O+x23z8Ms85Te9ZtNlvA9db22NF1ZmKm1taZiZkkZmptjx1dZyZmkpiprT1GmljMGN584f3FuhTc5+O1dXUB80eLvQchY7S1/t7WrS3yx5lkjPasN9/76T5Do70zec+Tw+FocW4j4WdDR9eZiZkkZmptjx1dZyZmkpiptT12dJ2ZzJ+pvfnC0h8UPnXqVO3YsUPPPPOMDhw4oKeeekpbt27V9ddfL6fTqeeee06S59VWV155pRwOh6ZNm6a8vDy9+eabqq+v18KFCzVp0qQwT2IePpQPAAAAaB0ZI7jIHwY+FBwAAMAclr5SIzU1VWvWrNFPfvIT3XfffTrrrLP00ksvacCAAVq6dKmmT5+uOXPmyG6365133pEkpaWlafHixbr66qvVo0cPJScna/ny5WGdwywECgAAAKBtZIzgIX8YKDQAAADMY+lSQ5LGjRunzZs3t1ifNm2a9uzZoy1btmjs2LG+D4mWpDvvvFOTJk3Szp07NWHCBPXo0cPMLYcFgQIAAABoHzLGmSN/GCg0AAAAzGX5UqMt/fr109SpUwPelpWVpaysLJN3FB4ECgAAACA4yBinR/4wUGgAAACYz9KfqYHTI1AAAAAAMAv5w0ChAQAAEB6UGhGMQGEoragIy3EBAACAWEH+MFBoAAAAhA+lRoQiUBgOlpZq9aZNYTk2AAAAEAvIHwYKDQAAgPCi1IhABAqDN1CkJCWF5fgAAABAtCN/GCg0AAAAwo9SI8IQKAxNA8WUcePCsgcAAAAgmpE/DBQaAAAA1kCpEUEIFIbmgSIhPj4s+wAAAACiFfnDQKEBAABgHZQaEYJAYSBQAAAAAKFF/jCQPwAAAKyFUiMCECgMBAoAAAAgtMgfBvIHAACA9VBqWByBwkCgAAAAAEKL/GEgfwAAAFgTpYaF1dXXEyhOIVAAAAAAoUWhYSB/AAAAWFdcuDeA1q3Ny9OxqioCBYECAAAACCkKDYMV8kddfb3pxwQAAIgUXKlhYRVOJ4HCAoECAAAAiGYUGgYr5I/aujqtzcsz/bgAAACRglLDwqaOH0+goNAAAAAAQoZCw2CF/OE9HxVOp+nHBgAAiBSUGhaWnpISluMSKAAAAIDoR6FhsEL+aHo+po4fb/rxAQAAIgWfqQE/BAoAAAAg+lFoGKyQP5qfD7ud1x8CAAC0hmdK8CFQAAAAANGPQsNghfxhlfMBAAAQKSg1IIlAAQAAAMQCq/wCnfzhYZXzAQAAEEkoNUCgAAAAAGJAXX29JX6BTv7woNAAAADoHD5TI8YRKAAAAIDYsDYvT8eqqig0LJA/KDQAAAA6jys1YhiBAgAAAIgdFU4nhYYF8geFBgAAwJmh1IhRBAoAAAAgtkwdP55Cg0IDAAAg4lFqxCACBQAAABB70lNSwnJc8ocHhQYAAEBwUGrEGAIFAAAAALOQPzwoNAAAAIKHUiOGECgAAAAAmIX84UGhAQAAEFyUGjGCQAEAAADALOQPDwoNAACA4KPUiAEECgAAAABmIX94UGgAAACEBqVGlCNQAAAAADAL+cODQgMAACB0KDWiGIECAAAAgFnIHx4UGgAAAKFFqRGlCBQAAAAAzEL+8KDQAAAACD1KjShEoAAAAABgFvKHB4UGAACAOSg1ogyBAgAAAIBZyB8eFBoAAADmodSIIgQKAAAAAGYhf3hQaAAAAJiLUiNKECgAAAAAmIX84UGhAQAAYD5KjShAoAAAAABgFvKHB4UGAABAeFBqRDgChUddfb3pxwQAAABiDfnDg0IDAAAgfCg1IhiBwqO2rk5r8/JMPy4AAAAQS8gfHhQaAAAA4UWpEaEIFB7eQFHhdJp+bAAAACBWkD88KDQAAADCj1IjAhEoPJoGiqnjx5t+fAAAACAWkD88KDQAAACsIS7cG0DHECg8mgcKu51+DgAAAAg28ocHhQYAAIB18JvgCEKg8CBQAAAAAKFH/vAgfwAAAFgLpUaEIFB4ECgAAACA0CN/eJA/AAAArIdSIwIQKDwIFAAAAEDokT88yB8AAADWRKlhcQQKDwIFAAAAEHrkDw/yBwAAgHXxQeEWVlBUpPyiIgIFgQIAAAAIOQoND/IHAACAtXGlhoVRaBAoAAAAADNQaHhYJX8UFBWF5bgAAACRgFLDwnKyswkUFggUAAAAQDSj0PCwSv54t6BA+ZQaAAAAraLUsLBR2dlhOS6BAgAAAIgNFBoeVskf3vORE6YsCAAAEAkoNeCHQAEAAADEBgoND6vkj6bnI1wvcAMAAIgElBrwIVAAAAAAsYFCw8Mq+cMK5wMAACBSUGpAEoECAAAAiBVW+AU6+cNghfMBAAAQSSg1QKAAAAAAYoQVfoFO/jBY4XwAAABEmrhwbwDhRaAAAAAAYkNBUZHyi4ooNCySPyg0AAAAOocrNWIYgQIAAACIHRQa1skfFBoAAACdR6kRowgUAAAAQGzJyc6m0LBA/qDQAAAAODOUGjGIQAEAAADEnlHZ2WE5LvnDQKEBAABw5ig1YgyBAgAAAIBZyB8GCg0AAIDgoNSIIQQKAAAAAGYhfxgoNAAAAIKHUiNGECgAAAAAmIX8YaDQAAAACC5KjRhAoAAAAABgFvKHgUIDAAAg+Cg1ohyBAgAAAIBZyB8GCg0AAIDQoNSIYgQKAAAAAGYhfxgoNAAAAEKHUiNKESgAAAAAmIX8YaDQAAAACC1KjShEoAAAAABgFvKHgUIDAAAg9Cg1ogyBAgAAAIBZyB8GCg0AAABzUGpEEQIFAAAAALOQPwwUGgAAAOah1IgSBAoAAAAAZiF/GCg0AAAAzEWpEQUIFAAAAADMQv4wUGgAAACYj1IjwhEoAAAAAJiF/GGg0AAAAAgPSo0IRqAwFBQVheW4AAAAQKwgfxgoNAAAAMKHUiNCESgM7xYUKJ9SAwAAAAgZ8oeBQgMAACC8KDUiEIHC4A0UOdnZYTk+AAAAEO3IHwYKDQAAgPCj1IgwBApD00AxilIDAAAACDryh4FCAwAAwBooNSIIgcJAoAAAAABCi/xhIH8AAABYR0SVGpMnT9by5cslSRs3blR2drbS0tK0aNEiv/u9/PLLOuecc9S/f3+9+OKLYdhp8BEoDAQKAAAABEMs54vTIX8YyB8AAADWEjGlxsqVK7V+/XpJUllZmaZNm6bp06dr8+bNWrlypTZs2CBJ2r59u2bOnKm5c+dq/fr1mjdvnnbt2hXOrZ8xAoWBQAEAAIBgiOV8cTrkDwP5AwAAwHoiotSoqKjQ/fffr/PPP1+SJ4D0799fc+fO1ZAhQzRv3jw9++yzkqSlS5dq4sSJmj17toYPH64f/ehHWrFiRTi3f0YIFAYCBQAAAIIhlvPF6ZA/DOQPAAAAa4qIUuP+++/XN7/5TY0dO1aStHXrVk2cOFE2m02SNGbMGG3ZssV32xVXXOH72qa3RZrSigoCxSkECgAAAARLrOaL06HQMJA/AAAArCsu3Bs4nQ0bNuitt97Sf/7zH919992SJKfTqWHDhvnuk5SUpJKSEt9tWVlZAW8LpLa2VrW1tb4/O51OSVJDQ4MaGhokSXa7XXa7XS6XSy6Xy3df73pjY6Pcbvdp1x0Oh2w2m+9xm65LUmNjo++/NptNqzdtUr/UVN00aZIcDocaTt0e53DI7XarsclebDabHHa7XG633x596y6XXE33aLMZMwVYb3S5VFtXp7+sX6+yo0d189VXKyM9XY0ul/9MdrtnplN7a7ouyW+Pba23NdPGLVu0saBAl48erUtGjlSjy9VipkaXyxdCzTpPp1uPi4vzzNRk3WazyeFwtNhja+tmf+8xEzMxEzMxEzMxEzN1ZKam94sUoc4XkvUyhjdfNH0u3/z5+OEjR3yFhjd/+J53BzFjtJipyb7q6uv1l/XrfYVG39RUv5wRzIzR1kz/V1joyx/jL7xQkjo9U9M9diQ3HT5yxLPe2Oh3biPlZ4N37+1ZZyZmYiZmYiZmYiZm8mpvvrB0qXHy5El9//vf1x//+Ef17NnTtx4XF6fExETfn7t06aLq6urT3hbIo48+qvnz57dYLywsVPfu3SVJffr00eDBg1VcXKyysjLffTIzM5WZmandu3ersrLStz5o0CClp6dr+/btqqmp8a0PHTpUycnJKiws9PvmGTFihBISEpSfny/JE4IGDhyoaqdT13/ta9q2f7/vvg67XbnnnqvK6mrtPHjQt941IUEjBw5UudOpvYcP+9Z7deum7MxMlVRU6EBFhW+9T1KSBvfrp+LSUpWdClmSlJmSosy0NBV98YWK9u1TQo8eunjQICV06SJJ2r5/v2rq6oyZMjKU3L27CouL/Z6IjzjnHCXExSl/zx6/v9ecwYNV19Cgbfv2tWumyooKbdm9WzkXXqjEnj2Vv2dPwJlq6+qUmpoqSaadJ99MOTmqq6vTtm3bjJkcDuXm5qqyslI7d+40ZuraVSNHjlR5ebn27t1rnKdevZSdna2SkhIdOHDAOE8mf+8xEzMxEzMxEzMxEzN1ZKbmQcnqzMgXkvUyhjdf7CktVb/U1BbPx+vq6vTeRx/prPR0DRk82Jc/gp0xdpeUqLLJ392gvn2V3quXtu/frxMnT+pgaakSevTQ9aNGKSM9XR999llIMkZbM71dUKCSo0d9+aO4tLTTM3U2N52srdXm/HzFx8drz549vn/XkfSzIRp/3jETMzETMzETMzFT6GcaOHCg2sPmtvDLqx566CF9/vnnWrlypSTp1ltv1eWXX64PPvhAaWlpeuSRRyRJx44dU0ZGhk6cOKEpU6boW9/6lmbNmiVJ+vjjjzVz5kz95z//CXiMQK+iGjBggI4cOaKkpCRJ5rdqhw4d0rJly/TdqVN1zllnBe0VR+19FVV9Q4NWrl2rsqNHNX3yZPXv0ydorzjqyKuo8j7+WO9s2aLLR4/WuFOvkGptpkNHjmjZq6/q9ttvV9++fWk/mYmZmImZmImZmImZTJjJ6XQqOTlZlZWVvufOVmZGvpCslzG8+eJ7112njD59POunjllSVqYX161Tn969NWPyZMXFGa97C8XV4IGuaqg+edJ3hfj0yZM1oG/fkF8NHmimvI8/1ob8fL/8caZXnzTdY3tm8p6PXj166HBFhWbNmqV+/foZM0XIzwYp+n7eMRMzMRMzMRMzMVPoZzpx4kS78oWlr9RYtWqVysrKlJycLEmqrq7WSy+9JEm65JJLfPcrLCxURkaGJCk3N1ebN2/2hY6mtwWSmJjo98orr7i4OL8n9JJxEprzfqO0d7354zZfd5x68p2YkCCbzaa4AI/T2rrdZpM90LrdHvADVJqvn+49bB0B5pcUcC8dXW8607sFBXpny5Y238O26d4ddrvvH4ZZ56k96zabLeB6a3vs6DozMVNr68zETBIztbbHjq4zEzNJ/7+9+46OovrbAP7sppJCQggQQktC7y0QehGUKkUUKdIUETtVxFdFUBFQivwEC0Ug0rsCoUoNIIQaIPQkdJIQ0kjP3vePzWxJL9v3+Zzjkdzdmb13ZrO5z36ncEwF9dGcGCJfAKaXMaR8IX2xDijn4w+jorAuKKjQe2iUNmOo+p7HOKVL3uaVP3SdMbT6mGNMhd1DozhjKqi9oL5r7o9urVtj1T//wMbGJte+NYfPhuK2c0wcE8Ax5dfH4rZzTBwTwDHl18fitnNMhh9TUfOFSd8o/Pjx47hy5QouXryIixcvol+/fpg1axbu3buH4OBgHDx4EBkZGZg3bx569OgBABg0aBA2bNiA0NBQJCUlYfHixarHqGC8KR8RERERWTLmCzXeFFzNFPJHzv1hb2dnlH4QERERmQOTPlOjatWqWj+7uLjA09MTnp6eWLhwIXr37g0XFxe4u7tj1apVAICmTZvi008/hb+/PxwdHVG7dm188MEHRui9eWGgICIiIiJLx3yhxIKGminkD1PYH0RERETmxKSLGjlJwQIAxo8fjx49euD69evo2LEjXFxcVI99//33GD58OB4+fIjOnTvDnpPCAjFQEBEREZE1ssZ8YQpfoDN/qJnC/iAiIiIyN2ZV1MjJ19cXvr6+eT7WoEEDNGjQwMA9Mj8MFERERERESpaeL6JiYxEUHMyCBkwjf7CgQURERFQyJn1PDdIvBgoiIiIiIuux+8QJFjRgGvmDBQ0iIiKikmNRw0oxUBARERERWRePsmVZ0DCB/MGCBhEREVHpsKhhhRgoiIiIiIisT6/27VnQYEGDiIiIyOyxqGFlGCiIiIiIiKyTvZ2dwV+T+UONBQ0iIiIi3WBRw4owUBARERERkaEwf6ixoEFERESkOyxqWAkGCiIiIiIiMhTmDzUWNIiIiIh0i0UNK8BAQUREREREhsL8ocaCBhEREZHusahh4RgoiIiIiIjIUJg/1FjQICIiItIPFjUsGAMFEREREREZCvOHGgsaRERERPrDooaFYqAgIiIiIiJDYf5QY0GDiIiISL9Y1LBADBRERERERGQozB9qLGgQERER6R+LGhaGgYKIiIiIiAyF+UONBQ0iIiIiw2BRw4IwUBARERERkaEwf6ixoEFERERkOCxqWAgGCiIiIiIiMhTmDzUWNIiIiIgMi0UNC8BAQUREREREhsL8ocaCBhEREZHhsahh5hgoiIiIiIjIUJg/1FjQICIiIjIOFjXMGAOFWlRsrFFel4iIiIjIWjB/qLGgQURERGQ8LGqYKQYKtYdRUdh94oRRXpuIiIiIyBowf6ixoEFERERkXCxqmCEGCjUpUHiULWuU1yciIiIisnTMH2osaBAREREZH4saZoaBQk0zUPRq394ofSAiIiIismTMH2osaBARERGZBhY1zAgDhVrOQGFvZ2eUfhARERERWSrmDzUWNIiIiIhMB4saZoKBQo2BgoiIiIhIv5g/1Jg/iIiIiEwLixpmgIFCjYGCiIiIiEi/mD/UmD+IiIiITA+LGiaOgUKNgYKIiIiISL+YP9SYP4iIiIhME4saJiw9I4OBIhsDBRERERGRfrGgocb8QURERGS6bI3dAcpfUHAw4hITGSgYKIiIiIiI9IoFDTVTyB/pGRkGf00iIiIic8EzNUxYbEICA4UJBAoiIiIiIkvGgoaaKeSPtPR0BAUHG/x1iYiIiMwFixomrE+HDgwULGgQEREREekNCxpqppA/pP0Rm5Bg8NcmIiIiMhcsapiwih4eRnldBgoiIiIiIsvHgoaaKeQPzf3Rp0MHg78+ERERkbngPTVICwMFEREREZHlY0FDzRTyR879IZfz+EMiIiKi/HCmRCoMFERERERElo8FDTVTyB+msj+IiIiIzAWLGgSAgYKIiIiIyBqYyhfozB9KprI/iIiIiMwJixrEQEFEREREZAXSMzJM4gt05g8lFjSIiIiISob31LByDBRERERERNYhKDgYcYmJLGiYQP5gQYOIiIio5HimhhVjoCAiIiIish6xCQksaJhA/mBBg4iIiKh0WNSwUgwURERERETWpU+HDixosKBBREREZPZY1LBCDBSmaefOnfDz84OtrS2aNWuGsLAwrcenTZuGV199Vavtzz//RKNGjeDu7o6hQ4ciJibGkF0mIiIiIjNS0cPDKK/L/KHEgobuMUMRERFZJxY1rAwDhWm6c+cOxowZgzlz5uDhw4eoU6cOxo4dq3r88uXLWLp0KX7++WdV28GDB/HJJ59g4cKFuHz5MhISEjBw4EBjdJ+IiIiIKE/MH0osaOgeMxQREZH14o3CrQgDhekKCwvDnDlzMHjwYADA+++/jz59+gAAFAoFxo0bh4kTJ8LPz0+1zJo1azB69Gi8/PLLAIAff/wRDRs2RGxsLDyMdBQeEREREZGE+UOJBQ39YIYiIiKyXjxTw0owUJi2vn37Yty4caqfb9y4gdq1awMAfvvtN4SGhsLHxwd///030tPTAQAxMTGoXr26ahkbGxut/xMRERERGQvzhxILGvrDDEVERGS9WNSwAgwU5iU9PR3z58/H+PHjkZSUhBkzZsDPzw+RkZFYuHAhOnTogJSUFLRo0QK7du2CQqEAAKxatQqtWrWCm5ubkUdARERERNaM+UOJBQ3DYYYiIiKyLrz8lIVjoDA/M2bMgLOzM8aOHYv169fjxYsXOHz4MDw9PZGZmYnGjRsjMDAQU6ZMwdGjR9GiRQuUKVMGp0+fxpo1a4zdfSIiIiKyYswfSixoGBYzFBERkXVhUcOCMVCYn3///RdLlizB6dOnYWdnhwcPHqBNmzbw9PQEANja2qJJkya4ffs23N3dcfz4cdy+fRs//fQT4uLiMGzYMCOPgIiIiIisFfOHEgsahsUMRUREZH14+SkLxUBhfsLDwzF06FAsWbIEDRo0AABUrVoVKSkpWs+LjIxElSpVVD97e3tj27Zt+OGHH3gtWCIiIiIyCuYPJRY0DIsZioiIyDqxqGGBGCjMT0pKCvr27Yv+/ftj4MCBSEpKQlJSEvr06YNr167ht99+w4MHD7B48WJcunQJr732mmrZ//3vf6hXrx4GDBhgvAEQERERkdVi/lBiQcOwmKGIiIisFy8/ZWEYKMzT/v37ce3aNVy7dg3Lli1TtYeHh2PPnj2YMmUKJk2ahMqVK2PTpk2oVq0aAOD58+eYN28e9u7da6yuExEREZEVY/5QYkHD8JihiIiIrBeLGhaEgcJ89e/fH0KIPB/z8fHBqVOn8nysXLlyePbsmT67RkRERESUJ+YPJRY0jIMZioiIyHqxqGEhGCh0Kz4+HsnJycbuhllxcnKCm5ubsbtBRERERAbA/KFkKQUN5h/zw/xFRETWjEUNC8BAoVvx8fFYsmQJMjIyjN0Vs2Jra4uPPvqIE2siIiIiC8f8oWRJBQ3mH/NjY2ODjz/+mPmLiIisEosaZo6BQildhxPw5ORkZGRkoGvfvihXvnyux6MfP8bx/ftztfcdOhT2RRi7EAIhwcG4f+eOVnuNWrXQol07yGSyQteRnp6OXevX52rv+MorqFC5cqHLA7ofR2ZmJpKTkzmpJiIiIrJgzB9KllLQAArPP4Bp5g+JuecoSXHGEf3kCY7v28f8RUREVotFDTPGQKGUlp6OoOBgna+3XPny8PTyytXu6eWFuk2b4lFkJJKTkuDp5QWPChWKte5er7+OtNRUPAgPhxAC1fz84ODoWKx1jJs2Dc9jYhD9+DHKODujio8P5HJ5kZfX5TiuXbiAs8eOFWtZIiIiIjIvzB9KllTQ0JRf/pGYUv6wlBxVmnEQERFZMxY1zBQDhZIUKGITEgz6unK5HFV9fUu1DgdHR9SsX79U6yjn6Ylynp4lXl5X46jm68uiBhEREZEFY/5QstSCRlGZSv6wlBxV2nEQERFZq6IfjkAmg4FCSTNQ9OnQweCvT0RERERkDZg/lKy9oEFERERkKnimhplhoFDKGSiKc7owEREREREVDfOHEgsaRERERKaD3wSbEQYKJQYKIiIiIiL9Y/5QYv4gIiIiMi0sapgJBgolBgoiIiIiIv1j/lBi/iAiIiIyPSxqmAEGCiUGCiIiIiIi/WP+UGL+ICIiIjJNLGqYOAYKJQYKIiIiIiL9Y/5QYv4gIiIiMl28UbgJOx8WhpCwMAYKIwUKIQSEEJDJZCVeh0KhgEwmK/E6pD6U5kboQggAMOo4iIiIiMj0saChZM0FDUvJHxwHERGRZWNRw4SxoGHcQLF9zRrVvweMGIGK3t5FWk4IgctnzuC/I0dUbeU8PdH/rbdg7+BQpHWkp6UhaNMmPH30SNXWvG1b+HfsWOQJcfTjx1pjAAw/DiIiIiIyDyxoKJlKQeN8WJjBX/PI7t2IjYlR/WyO+cNScpQuxkFERGTJWO43Yf716zNQmECgAIAdgYGIfvKkSM89smeP1gQWAJ7HxGDVokXIyswsdPmszEysWrRIawILABdOncKxoKAi9SHq0aNcE3HAsOMgIiIiIvPAgoaSqeSPY+fPI8QIRQ3NggZgfvnDUnKULsZBRERk6XimhglrUb++UV6XgULptVGj4Onlhbs3buDgjh3Yvno1xk2bVuAyaampuHXlCgDgrQ8/hJOLC4QQ2LJyJZ7HxCD03Dk0CwgocB1Xzp0DAHh6eWHAiBGQy+VIefECgb/8ghuhoWjz0ktwcHQscB07AgMBAN0HDIBf3boAYPBxEBEREZHpY0FDyRTyB6DeH/716xu8sOFevjxef/tts80flpKjijIOIiIia8czNUgLA0Vu0mQWUF9XNT8PwsMBAE0DAuDk4gJAeQ3WV4cNAwCcyXHETl7OHD0KAOg5aJDq2qllnJ3RvG1bAMD9u3cLXF6zj5p9N/Q4iIiIiMi0saChZCr5Q3N/GOMAt7bdupl1/rCUHFXacRAREVkDFjVIhYGi9BQKBQDkupGbIisLACArwg3epImy3MZGqz1LWocBrqGqi3EQERERkeliQUPJVPKHKeyPnHN/c8sflpKjTGEcREREpo7fTBIABoqC3L1xQ/XvwiaQ1WvWBKC83mnKixcAlBPbvVu2AAACOncu9PVaZz/nn3XrVBPa5KQkXD5zBgBQ1de3wOU1+6jZd0OPg4iIiIhMkyl8gc78oWYK+wMAju3da9b5w1JyVGnHQUREZA14Tw1ioMjHttWrtX4eMGJEocs4ODqiTuPGuBkaisBffsn1eMMihJTG/v44c/QonsfEYNm8eVqP1W7UqNDrwALAwFGjsH31ahzcsSPXY4YaBxERERGZHlP4Ap35Q80U9ockMS7OrPOHpeSooowjsdC1EBERWTaeqWHlGCiKZuCoUajo7V2k53bu1Qst2rXTaqtUpQpGT5gAG9vC64g2trYYPWECynl6arUHdOmCLr17F6kPFbJvKpeTIcdBRERERKblfFiY0b9AZ/5QM6WCBgC4urtr/Wxu+cNScpQuxkFERGTp+M2kFWOgKNjAkSPh6eVV7GuWymQy+HfsCP+OHaFQKCCTyYq9DnsHB7zxzjsQQkAIkeuarEVR0dsb46ZNU52ybIxxEBEREZHpCAkLY0HDRPKHqRU0AODl/v1RvlIls84flpKjdDEOIiIiS8aihpVioCicLr7EL+3kUxd90EUhgpNoIiIiIvPnX78+CxomkD9MsaAhsZT8wXEQERFZNn5TaYUYKIiIiIiIrE+L+vWN8rrMH2qmXNAgIiIiMhcsalgZBgoiIiIiIjIU5g81FjSIiIiIdINFDSvCQEFERERERIbC/KHGggYRERGR7rCoYSUYKIiIiIiIyFCYP9RY0CAiIiLSLZMvauzcuRN+fn6wtbVFs2bNEBYWBgC4cuUKWrVqhXLlymHq1KkQQqiWOXr0KOrXrw9PT08sWLDAWF03GQwURERERERqzBj6xfyhxoIGERERke6ZdFHjzp07GDNmDObMmYOHDx+iTp06GDt2LNLS0vDqq6+iZcuWCAkJwbVr17Bq1SoAQHR0NPr164ehQ4fi1KlTWLt2LQ4fPmzcgRgRAwURERERkRozhn4xf6ixoEFERESkHyZd1AgLC8OcOXMwePBgVKpUCe+//z4uXLiAoKAgxMfHY8GCBahZsyZmz56NFStWAADWrl0Lb29vfPXVV6hduza+/vpr1WPWhoFCfxQKBe7fvYuboaF4HhNTonWkpqTg1tWruBMWhrTU1BKtIzY6GjdCQ/EgPBwKhaLYy+tqHPfu3i3RskRERESGxoyhP8wfapZa0DCV/GEpOaq04yAiIrJWtsbuQEH69u2r9fONGzdQu3ZtXLp0CW3atIGTkxMAoEmTJrh27RoA4NKlS+jatStkMhkAoHXr1vj8888N23ETwEChP48iI7Frw4Zc7aM+/RQOjo6FLi+EwNGgINwMDdVqr92oEbr07q167xYkLTUVq3/+OVd73yFD4F2jRqHLA/obBxEREZEpY8bQD+YPNUssaJhy/rCUHFWccaSnpxepr0RERJbKpIsamtLT0zF//nxMmjQJt2/fhq+vr+oxmUwGGxsbPH/+HAkJCWjQoIHqsbJly+LRo0f5rjctLQ1paWmqnxMSEgAAmZmZyMzMBADI5XLI5XIoFAqtozik9qysLK3r7ebXbmNjA5lMplqvZjsAZGVlqf4vk8kghIAQAlk5jhyxtbHJ1S6TyWAjl0MhBB48fYr1e/eioocHhvToAQd7e2XfNfsok6nHlEd7lkKRe0wyWa52G7lcOabsvksyMzOxbu9eRD9/jmG9eqFS+fLIzMqCjVx5clBxx6TIq12j71kKhWryV9r9JP1boVCo9on0/BeJidi9cSNkMhmq+Pigqq8vTv/7LwBgzeLFeHvyZPW2yR6TVt8BnAsOxs3QUMhkMjQNCIBQKHD57FncunIF5cqXR+NWrbTGKu0nzT5KE3GZTIa23brh3p07eBgRgV0bNmD4Bx+gjJMT1M9W9l0mk6nGk5w9DgCo6uuLqr6++C/7EgprFi/G2KlTVdtAk+aYzp04gVtXrkAmk6FOo0a4ERqKrKws1ftb+r3Mb38Y6vepsHZbW1vle0+jPb++c0wcE8fEMXFMHBPHpN13zeeZI2vJGFK+0JzL63I+/ig6WpU/hvfqBTtbW618oKuMkV+WsJHLkZaenit/AChVxtDse1FzU/DFizhy7hy6tGyJdk2bqsZQ3DGp9pPG/BrQz2eDZv4EAEVWlnaWkMmw+uefVXlLyh8PwsOxa8MGDH3vPTi5umqtH9DOEsmJiapCQM78sfrnn/HuZ5/lyk1yGxsIje0u5Q8AaNamjSpH3b56FeXKl0ezNm1y5SZVnsoe05rFi1Vjbf/yy4i8fRsPIyKwe+NGDBk3Di5ublq5KeeYkuLj88yDMplMKw/mlwXlNjY4e+yYKkdJefDSmTO58mB+WVAmk2HX+vWqfub8TLH2v0scE8fEMXFMHJN5j6mo+cJsihozZsyAs7Mzxo4diy+//BIODg5ajzs6OiI5ORm2trZaj0nt+fnhhx8wc+bMXO0XLlyAs7MzAKBChQqoWbMmwsPDER0drXpO1apVUbVqVdy8eRPx8fGqdj8/P1SsWBFXrlxBSkqKqr1evXpwd3fHhQsXtN48TZo0gb29PUJCQgAoQ5CPjw8UQiAlPR2XIyNVz7WRy9GqVi3EJyfj+sOHqvYy9vZo6uOD6/fu4fzt2/Dz80OVihURGROD+lWr4lFsLB7ExqqeX6FsWdT08kJ4VBSis0MWAFT18EBVT0/cfPQI8Rrbza9SJVR0c8OVe/eQonFUSL0qVeDu7IwL4eGqibhCocDNW7cQ/fw5Alq2xKOEBDzKfg3/mjWRnplZrDHFJCTg7tOnqnY3J6dcY0pLT0f57OBS2v3k6ekJAIi+dw9xT56o2mvUr4+Thw7Bx8cHHhUqwNXNDQDw9uTJCFy8GFWrVkXYmTOwd3CAXC5HrWbNkJyYiIe3b6vWYe/oiAunTsHV1RXN/P1hY6v8Fezepw8O7NqFm5cuwUnjqLay5cvDq0YNRN2/j4RnzwAA6WlpcHd3R1xcHHoNHIjkxETUqlULFT09cev6dRwNCkKDRo2QrnEKc5VateBctizCQ0OhUCgQ/eQJfHx8UNnPD/WbN8edS5fQKTucAMCzqCi4li2LyOybZgLINab4p0/h4+MD37p14V6pEh5FRODOnTt48OCBcj+5uaF+/fp49OiRqg0w/O+TxN/fH+np6bh8+bKqzcbGBq1atUJ8fDyuX7+uai9TpgyaNm2KmJgY3NW4tBbHxDFxTBwTx8QxcUzaY8oZlMyNtWQMKV/ciYqCV/nyOp2P3378GA+iouDn54cmNWvCwd4ed5480XnGAIAmNWrA3tYWIdlzVlV79erYdOAA7F1cEODnh0cJCXialFTqjAEULzfFxsfj+u3b6OrvDzcPD61+FndMVd3dYWdnpzW/1tdng/T+eBEXhwqVK+PR3btITkxUPd8pO/dUqVIFNevVg0wmQ61atVC1enWcPnoUF48fR6UqVVTPr1G/Pmzt7XHn0iVVW/STJ8oDsl56CfbZBYJO3boh4tYtREZG4vG9e0jKzjuAMjf5NGiAhNhYPL13DwAQ//QpKlWqhJdffx0v4uMR++QJOnTpggcREbh79SqatWmjlZsAwMPLC57e3nh09y7iYmLg4+MDAGjVtSvcK1SAs6MjKnp64nlMDC6dOIHmnTpp5aacY7p88qRWHqzZtCnqNGyI47t2AQDCzpyBY5ky+WZBnwYNcOfqVfj4+KCqjw9sbG3h5OqKxq1aYdfatYi+dw93svNgXlkQAByyP0MqVaqk9f7g3yWOiWPimDgmjskSxiT9rS6MTJjB4VX//vsvBgwYgNOnT6NBgwaYO3curly5gsDAQNVz3N3dcevWLXz99dfw9PTEt99+CwCIi4tDlSpV8OLFizzXnddRVNWqVcOzZ89QtmxZAIavqj158gQrV67E2AEDUNnTs8hHUT2JiVGd8j2kRw/Y29np7Iijoh5FlZ6RgQ379iEqNhZv9e4Nr+wCgebzAd2fqfHk2TOs3LkT7777LipVqlSq/RQVFYVly5ZhwIgRKF+pktbzl82bB5lMhpEffwzb7MmmXC7H1fPncerQIXTq0QO1GjVSjjWPo3OEQoGVCxYAAN6ZMkU9JgDLf/oJADBm0iTVkUB5HZ1z+8oVHN27F227dUPD5s1VR1FlZWZi1aJFAICxU6YUeKbGyvnzAShPkbbLPpMHAMIuXlSOo2dP1GncWKvvmmPKzMzEqoULleOYPBmx0dHYtno13nnnHXh5ean3E6vUHBPHxDFxTBwTx2TRY0pISIC7uzvi4+NVc2dzYU0ZQ8oXb/fvjyoVKijbdTAfv//0KdYFBaFCuXIY0qMHHO3t9ZIxNNtz9l0zfwzr1Qve2eMr6ZhKmpuCL17E0fPn0blFC3Ru2bLUZ59ExcZi2fbtWvNrQD+fDdL7Y8CIEahQuXKuMzVuX72Ko0FBaPPSS2iocTkthUKBPxcsgEwm0zpjXcoymq+5cv58CCEwesIE1YFdgDJ/nDx4EJ179UKthg3VfYf2mRoKhUKVP6SzOqTtK2UbKV/ld6bGrStXcGzfPgR07YrGLVtClt2emZmpOhN+7NSpBZ6psfzHHwFAlQel9px5ML8zNSCTYfmPP2ptM2msf8ydC5lMhtETJ6ryW84sqLk/ZDIZ3n77bdX7g3+XOCaOiWPimDgmSxjTixcvipQvTP5MjfDwcAwdOhRLlixRnfLdqlUrLFu2TOs5aWlp8PDwQKtWrbBu3TrVYxcuXEAVjaNGcnJwcMh1RBag3Mm2ttqbR9oJOUlvlKK251xvznZpAiSTySCTyWCbx3pythd2DVu5XJ7nXeHza7fJY5wFtdva2CAtPV0VKAq7hm1RxqTqo0wGeV7tGn23kctVvxil3U/SadVyuTzXYw5lyiAtJQVJiYkorzG+e7dvQwgB57JltZaRPgAkQqNf0iQV0J5429raqvqgOVaJdGr3/bt30djfX9Uu3aCubLlyeW4vzbHaOzoiLSUFifHxKF+xoqpdNQ5X11x91+y39CGY3aC1/qL+3hjq96ko7dKYitpHjoljyq+PxW3nmDgmgGPKr4/FbeeYjDOmnHMWc2FtGUPKF9IX60Dp5+MPo6Kwds+ePPOHLjNGQe1FyR+lyRhFabeRy3Hs/HkcOXdO6x4aJR2TZr+BvOfXuv5s0MyfAHJtFycXFwDAg/BwNNG4VK6UP1zd3fN8X2q25cwfknvZZzI4u7rmnT3kcthk91V1eSyFQitHSe1Sfs6L3MYGTq6uEEJojUNuY4OEmBgIIVC2XDmt7Z4XaRxFyYN55Smpr9L2zpkHhRC58mDOfSftDyFEnu8Pa/+7VNx2joljAjim/PpY3HaOiWMCSj+mouaLvGdaJiIlJQV9+/ZF//79MXDgQCQlJSEpKQkdO3ZEQkIC/vzzTwDA7Nmz0b17d9jY2KBfv34IDg7GwYMHkZGRgXnz5qFHjx5GHol+8aZ8hvPKwIEAgK1//omYp0+RmZmJKyEheBARAQCF3lxOJpOpTs3esWYNkpOSkJqSgi0rVwIAynl6FvrLWyX7NKwH4eG4duECMtLTEfP0KbatWgUA6NSzp8HHoXn6GBEREZEpY8YoPeYPNUu8KXhOppg/LCVHlWYcRERE1sykz9TYv38/rl27hmvXruU6amr58uUYOnQopk6dCrlcjiNHjgBQ3g9h4cKF6N27N1xcXODu7o5V2ZMUS8RAYViVq1WDX716uHv9umryK+k7dGieFcqcer3xBlYtWoSYp0/x15IlWo/1f+utQpeXy+XoO2QIdm3YgBP79+PE/v2qx/zq1YN39eoGH0fQpk2FPp+IiIjIFDBjlA7zh5o1FDQA08wflpKjSjqOjq+8guMa/SciIrI2Jl3U6N+/f753PPfx8cGdO3dw7tw5tGnTRnWTaAAYP348evTogevXr6Njx45wyT4909IwUKid17ihtb51798fj5o3x7G9e5Hw/Dmq+vqic69ecM6+LFRh7B0c8M7kybh6/jz+O3oUQqFA686d0VjjxuGF8a5RA8M/+AAnDx1C+I0bcCxTBi8PHIjK1aoZZRynjxwBTP/2PERERETMGKXA/KFmLQUNianlD0vJUSUdR4XKlYvcXyIiIktk0kWNwnh5eaFPnz55Pubr6wtfX18D98hwGCjUjp0/jxADFjUAwLt6dQwZN67Ey9vY2qJJ69Zo0rp1idfh7OqKlwcMKPHygO7G4V29OratXl2qvhARERGZAmvOGAVh/lCztoKGxJTyh6XkqNKMg4iIyJqZ9D01KG8MFGpSoPCvX98or09EREREZOmYP9SstaBBREREZEpY1DAzDBRqmoGiBYsaREREREQ6x/yhxoIGERERkWlgUcOMMFCoMVAQEREREekX84ca8wcRERGR6WBRw0wwUKgxUBARERER6RfzhxrzBxEREZFpYVHDDDBQqDFQEBERERHpF/OHGvMHERERkelhUcPEMVCoMVAQEREREekX84ca8wcRERGRabI1dgcof1GxsQgKDmagAAMFEREREZG+saChxvxBREREZLpY1DBhu0+cgFf58gwURgoU21avhkwmQ+vOndGoZUvY2Bb91+VFYiKO7tmDBxERKOvujk49e8K7Ro1ivf7j+/exf/t2pKWkwKdOHbTv3h3Orq5FXj4rMxOhISE4c/SoUcdBRERERKaPBQ01UyhoRMXGGvw1o58+xe5Nm8w+f1hKjirtOIiIiCwZixomzKNsWQYKIwcKIQT+O3IE/x05gtETJsDewaHQZR5FRmLXhg2qnxPi4rBrwwb41auH7v37F+l1D+7cibvXr6t+jrh5ExE3b6LvkCFFmgynp6Vh1aJFRh8HEREREZk+FjTUjJ0/AOX+2H3ihMFf9/jevap/m2v+sJQcVdpxEBERWTreU8OE9WrfnoHCiIGiz5AheOvDD+FZqRIAIGjz5kKXUSgUqglsu+7d8fbkyRg0ZgwA4O7163h8/36h63gUGamawA4aMwZjJk5Eh1deAQDs2rABCoWi0HXs/OsvAEA5T0+M/OQTo4yDiIiIiEwfCxpqxs4fgHp/eJQta5TXN+f8YSk5ShfjICIisnQsapgwezs7g78mA4Wag4MDnFxcMGDkSADA04cPIYQocJlHkZEAgKq+vmjUsiVsbW1RvmJFvDZ6NADgwPbthb7usewjpAaNGYPyFSvCzt4eDZo3R1VfXwDAw4iIApcXQuB5TAwA4PW334ZjmTJGGQcRERERmTYWNNRMIX9o7o9e7dsb/PVfevVVs84flpKjSjsOIiIia8CiBqkwUORNLlf/mhQ2iX2RlAQAqFGrlla7W7lyAIDUlJRCXy8hLk65jIeHVnu17ElsyosXBS6v2UeZTKb6t6HHQURERESmiwUNNVPIHzn3hzEOcHNxc9P62dzyh6XkqNKOg4iIyBqwqEEAGCgKojlp1JzQ5qWClxcAIPjAAa0J762rVwEAvnXrFvp6VX18AADXL11StQkhcOrff5WvUblygctr9jE5e1INGH4cRERERGSaWNBQM4X8YQr7A1Det0FijvnDUnJUacdBRERkDXijcGKgyMflkBC4li2LC6dOAQBatGtX6DIeFSqo/r1s3jy0fekl3A8Px4PwcABAu27dCl1H5969sXbpUpw8eBD3bt9Gjdq1EXzggOrxcp6eha4joEsX/HfkCP5asgRNWreGjY2NwcdBRERERKbHFL5AZ/5QM4X9Ibl85gxio6LMNn9YSo4qyjhinjwpdD1ERESWjGdqWDkGivzdvnpVNYGt27gxWnboUKTlRn36qerfp/79VzWB7TtkCJxdXQtd3tnVFX2HDAEAPIiI0JrAaq67IE1at0btRo0AKMOJMcZBRERERKYlKjbW6F+gM3+omVJBQ2LO+cNScpQuxkFERGTpeKaGFWOgKFirTp3g6uaGan5+cHB0LPJyDo6OGDdtGp7HxCD68WOUcXZGFR+fQk9V1uRdowbGTp2KR5GRSE5KgqeXl9ZRP4WRyWTo2qcP2nXrhgfh4RBCGGUcRERERGQ6dp84Aa/y5VnQMIH8YYoFjQEjRyI9NdWs84el5KjSjoOIiMjSsahhpRgoClfN1xee2ddELYlynp5FOsU5P3K5HFWzbwZXUg6OjqhZv36p1lHacRARERGRafAoW5YFDRPIH6ZY0AAAuUxmEfnDUnKULsZBRERkqXjItRVioCDKW0xMDHx9fREREVGkdk09e/bEqlWr9No/IiIiotLo1b49CxosaBBZFWY8IiLLxKKGlWGgIMpbTEwM+vbtm+dkN692TWvXrsW+ffv020EiIiKiUrK3szP4azJ/qLGgQWRYzHhERJaLRQ0rwkBBlL8hQ4Zg2LBhRW6XxMbGYvLkyahbt64+u0dERERkdpg/1FjQIDI8ZjwiIsvFooaVYKAgKtiyZcvwySefFLldMnnyZAwcOBBt2rTRZ/eIiIiIzArzhxoLGkTGwYxHRGS5WNSwAgwURIXzzecmfPm1A8Dhw4dx6NAhzJs3T1/dIiIiIjI7zB9qLGgQGQ8zHhGR5WJRw8IxUBDpR2pqKt577z38+uuvcHV1NXZ3iIiIiEwC84caCxpE5oUZj4jIfLCoYcEYKPRLCAGFQlHqdQghSrUOhUJRqnXoahzW5ttvv0WrVq3Qp08fY3eFiIiIyCQwf6hZckHDVPKHpeQoa8xSpooZj4jIfNgauwOkHwwU+pOeloagTZvw9NEjVVvztm3h37EjZDJZkdYR/fgxtq9Zo9U2YMQIVPT2LtLyQghcPnMG/x05omor5+mJ/m+9BXsHhyKtQ1/jsBbr1q1DdHQ03N3dAQDJycnYtGkTzpw5g6VLlxq3c0REREQGxvyhZokFDVPOH5aSo4o7DtI9ZjwiIvPBMzUsEAOF/mRlZmLVokVaE1gAuHDqFI4FBRVpHVGPHuVZCNgRGIjoJ0+KtI4je/ZoTcQB4HlMDFYtWoSszMxCl9fnOKzF8ePHceXKFVy8eBEXL15Ev379MGvWLMyaNcvYXSMiIiIyKOYPNUssaACmnT8sJUcVZxwhwcFFeh4VDzMeEZH54JkaFoaBQr+unDsHAPD08sKAESMgl8uR8uIFAn/5BTdCQ9HmpZfg4OhY4Dp2BAYCALoPGAC/unUBAHdv3MDBHTuwffVqjJs2rcDl01JTcevKFQDAWx9+CCcXFwghsGXlSjyPiUHouXNoFhBg0HGUdXPDttWrC3y+palatarWzy4uLvD09ISnp6eRekRERERkeMwfapZa0DDF/GEpOaqk47h/506Bz6GSYcYjIjIfPFPDgjBQ6N+Zo0cBAD0HDYJcrvz1KePsjOZt2wIA7t+9W+DymqcJSxPYnP8u7FTiB+HhAICmAQFwcnEBAMhkMrw6bJiyjzmOPDLkOCyBEAI+Pj5FbpesWrUKo0eP1lu/iIiIiEwN84eapRY0ANPOH5aSo0oyDio6ZjwiIsvDMzUsBAOFYUgTTLmNjVZ7VlYWABT5GqqlId2MTppEq9qlPsgLr1WawjgKEh8fj+TkZKP2wdw4OTnBzc3N2N0gIiIiK8H8oWbJBQ3AcvKHpY3DkJjPzA/zIRFZOhY1LAADheG07twZZ44exT/r1uH1t9+GTCZDclISLp85AwCo6utb4PKak9y7N25onW6c13PyUr1mTQDK67Y2atkSZZydoVAosHfLFgBAQOfOBh9HWR1OluLj47FkyRJkZGTobJ3WwM7ODh9++CEnrkRERKR3zB9qll7QAEwzf1hKjirNOAyF+cw8MR8SkaVjUcPMMVAYVmN/f5w5ehTPY2KwbN48rcdqN2pU6PVTAWDgqFHYvno1Du7YkeuxASNGFLq8g6Mj6jRujJuhoQj85ZdcjzcswvbX9zhKIzk5GRkZGejaeyDKeVQo1boyMtIRfCgICXGx6NC9Dzw8K+bZpm9hoecRdikE9Zv6o37jFvm2ldTz2Ggc3rMdycnJnLQSERGRXjF/qFlDQQMwj/xhKTmqqOOoXqsW7t2+XehzdaGk+Sw2JgonDu5GWXcPtO/WC3Z29nm26ZO15MGcmA+JyBqwqGHGGCjUomJjDfI6Nra2GD1hAnb+9Reex8So2gO6dEGT1q2LtI4K2TeVk24QJxk4ahQqeHkVaR2de/WCi6srzp88qWqrVKUKer3xBmxsC/+11uc4dKWcRwV4Vqpc4uXT09MQtHUtEhPi0HfwKFSsXEX1WL+hYxC0dS2C/w1Cn9dHaD2ma+dPH0PYpRD4t++KFm06qdo7VuoDZxdXhAQfhrOLq9ZjRERERKaI+UPNWgoaElPOH5aSo4ozjpbt2hmsqCEpTj6LevwQwf8GoXxFL/QaNBz29g4AAM9KleHu4YndWwJx5vghrcd0jXmQiMiysahhphgo1B5GRWH3iRMGez17Bwe88c47EEJACJHrmqxFUdHbG+OmTVNdk7W4116VyWTw79gR/h07QqFQQCaTFXsduhxH9OPH2L5mTbGX1xdpAhsbE5XnJNXe3gG9Bg1H0Na12L0lUG8T2fOnjyEk+HCuCaxEagsJPqz1MxEREZGpYf5Qs7aCBmB6+cNSclRpxmGqoh4/xO4tgfDwrJhn0aJi5Sro8/oI7N4SiKCta/VS2GAeJCKyfMX/60tGx0ChJgUKj7JlDf7aMpmsRBPYnOso7YRULpeXah26GoepKGwCK5Emsh6eFbF7SyCiHj/UaT8Km8BKWrTpBP/2XRESfBjnTx/TaR+IiIiIdIH5Q80aCxo5mUr+sJQcZUpZqrQKK2hIpMJGbEwUgrauRXp6ms76wDxIRGQdWNQwMwwUapqBolf79kbpA5mWok5gJfqayBZ1AivhRJaIiIhMFfOHGgsaRPkrakFDoo/CBvMgEZH1YFHDjDBQqOUMFPZ2dkbpB5mO4k5gJbqeyBZ3AivhRJaIiIhMDfOHGgsaRPkrbkFDosvCBvOgWmxMVKmWJyIyByxqmAkGCjUGCsqppBNYia4msiWdwEpY2CAiIiJTwfyhxvxBlL+SFjQkuihsMA+qRT1+iBMHd5doWWu2atUq1eXgNP9btWoVjh49ivr168PT0xMLFiwwdleJKBuLGmaAgUKNgYJyKu0EVlLaiWxpJ7ASFjaIiIjI2Jg/1Jg/iPJX2oKGpDSFDeZBNWl/lHX3KPHrW6thw4bh+fPnqv/u378PT09P1K9fH/369cPQoUNx6tQprF27FocPHzZ2d4kILGqYPAYKNQYK65KRkV7oc3Q1gZWUdCKrqwmshIUNIiIiMhbmDzXmD6L86aqgISlJYYN5UE1zf7Tv1qvUfbA29vb2cHd3V/23Zs0aDBw4EKdOnYK3tze++uor1K5dG19//TVWrFhh7O4SEVjUMGnpGRkMFNkYKKxP8KGgAieyup7ASoo7kdX1BFZSnImsJV8zddq0aXj11VdVP+/cuRN+fn6wtbVFs2bNEBYWZsTeERERWRYWNNSYP4jyp+uChqQ4hQ3mQbWc+8POjp9XpZGamoqff/4ZX3zxBS5duoSuXbtCJpMBAFq3bo1z584ZuYdEBLCoYdKCgoMZKGC8QHHv7l3cCQtDWmpqiZaPjY7GjdBQPAgPh0KhKPbyCoUC9+/exc3QUDyPiSlRH1JTUnDr6lWjjqOkEuJi853I6msCKynqRFZfE1hJUSaylnzN1MuXL2Pp0qX4+eefAQB37tzBmDFjMGfOHDx8+BB16tTB2LFjjdxLIiIiy8CChpopFDTSMzIM/poKISwif1hKjtLFOPRBXwUNSVEKG8yDavreH9Zo3bp1CAgIgI+PDxISEuDr66t6rGzZsnj06JERe0dEEltjd4DyF5uQgFF9+zJQGClQhBw/rvp37UaN0KV3b1V1viBpqalYnf0lrKa+Q4bAu0aNIr32o8hI7NqwIVf7qE8/hYOjY6HLCyFwNCgIN0NDtdoNPY7S6NC9D4L/DULQ1rVakzN9T2Al0kQ2aOta7N4SmOu19D2BlUjrDgk+rPUzoH3N1NiYp3rrgzEoFAqMGzcOEydOhJ+fHwAgLCwMc+bMweDBgwEA77//Pvr06WPMbhIREVkEFjTUTKGgkZaejqDgYIO/7o41a3K1mVv+sJQcVdpx6IuhvkCXChu7twQyDxYhD7KgoVu//fYbvvnmGwCAra0tHBzU29XR0RHJyclG6hkRaeKZGiasT4cODBRGDBR1GzdG04AAAMCtK1dw+cyZIi2nOYFt//LLqJpd1d+1YQNeJCYWuvyLxETVBLaqry/aduuW57oLEnL8uGoi3rxtW6OMo7Q8PCvmOkLHUBNYSX5H6BhqAivJ6wgdS79m6m+//YbQ0FD4+Pjg77//Rnp6Ovr27Ytx48apnnPjxg3Url3biL0kIiIyfyxoqBk7fwDq/RGbkGDw1wbMO39YSo7SxTj0ITYmyqBfoOd1xgbzYN55kAUN3bl9+zZu376Nl19+GQDg4eGB6Oho1eOJiYmw5+UIiUwCz9QwYRU9PIzyugwUSg1btICnlxca+/vjryVL8N+RI6pJbX5iNf7YvfvZZ5DJZGjYogWuhITg5KFDOBoUhN7ZR5nnJ/jgQQBAh1deQYPmzQEAjVq2xLJ581Sv4VGhQoHruHDqFABgxEcfoYyzMwAYfBy6oHmEzt8b/gQAJMbHGWQCK8l5hI53NR9E3rlhsAmsRPMInegnj/DofoRqApvwPNZg/TCEpKQkzJgxA35+foiMjERgYCC+++47HD16FGXKlAEApKenY/78+Zg0aZKRe0tERGS+WNBQM4X8obk/+nTogB1Hjhj09Zu1aYPWnTsDMM/8YSk5qijjMIYTB3ejfEUvg36BzjyoVlAeZEFDdzZt2oS+ffvCzs4OANCqVSusW7dO9fiFCxdQpYph3ntEVDCeqUFaGChyc3JxUf27sGuhxjx5AgBo262b1qnJ9Zo1AwA8CA8v9PUibt4EANRu2FDVJpPJ0D77SAHpNfKj2UdpIg4Yfhy6UrFyFbzSbzBio58iNvopXuk32GATWIk0kc1IT0PknRuoUbOuQSewkhZtOqFGzbqIvHMDGelpFjuB3bZtG168eIHDhw9j5syZOHDgABITExEYGKh6zowZM+Ds7Mx7ahAREZUQCxpqppA/cu4PYxzgVi37kp+AeeYPS8lRpR2HvmRmZhglfzAPqllLHjSmvXv3okuXLqqf+/Xrh+DgYBw8eBAZGRmYN28eevToYbwOEpEKixqkwkCRNyGE6t+FXUNVmvzev3tXqz3u2TMAQNly5Qp9PYfsI9ET4uK02iNv3QKgPanOi2YfNSfdhh6HrqSnpyHk5BHVzyEnj+R5szh9u3L+P9W/H92PyPdmcfoU9fghHt2PyLNPluTBgwdo06YNPD09ASivY9qkSRPcvn0bAPDvv/9iyZIlWLduneoIGiIiIio6FjTUTCF/mMr+yHlpJHPLH5aSo0o7Dn0yRv5gHlSzljxoLCkpKfjvv//Qrl07VZunpycWLlyI3r17o1KlSrhx4wa+/PJLI/aSiCQsahAABoq8pKSkIDUlBVtWrgQAlPP0LHQSW8XHB4DyCJxrFy4gIz0dMU+fYtuqVQCATj17Fvq6rwwcCADY+uefiHn6FJmZmbgSEoIHEREAUOjN5WQyGSplnw65Y80aJCclGWUcuqB5zdQBw8ZiwLCxWtdUNRTNa6aO/vjzXNdUNQTNa6aO/vjzXNdUtSRVq1ZFSkqKVltkZCSqVKmC8PBwDB06FEuWLEGDBg2M1EMiIiLzZSpfoDN/KJnK/gCAf//5x6zzh6XkqNKOQ1/qN/U3eP5gHlSzpjxoLGXKlEFaWhrq1aun1T5+/HjcuHEDa9euxeXLl1GpUiUj9ZCINPGeGsRAkY+gTZu0fu7/1luFLiOXy9F3yBDs2rABJ/bvx4n9+1WP+dWrB+/q1QtdR+Vq1eBXrx7uXr+umvxK+g4dCrm88FpkrzfewKpFixDz9Cn+WrLEKOMorYyM9DxvAiddUzVo61qDnG6b103gNK+paojrueZ1EzjNa6q+SNL/jdsNqU+fPvj444/x22+/oW/fvti2bRsuXbqEzZs3o2fPnujfvz8GDhyIpKQkAICzs3OhAZOIiIiA9IwMk/gCnflDyZQKGhJzzh+WkqN0MQ59qN+4BZxdXBESfBgA9H75pfxuCs48mDsPAvrfHwWJj49HcnKy0V7fEBwdHdGsWTMkJiYiMdH887eTkxPc3NyM3Q2iUmFRw8oxUBRAJgOEQOvOndHY3x82tkX7dfGuUQPDP/gAJw8dQviNG3AsUwYvDxyIytWqFfmlu/fvj0fNm+PY3r1IeP4cVX190blXLzi7uhZpeXsHB7wzeTKunj+P/44ehVAojDKO0gg+FITEhNw3gdO8WZy+J7J5TWCB3DeL0+dENq8JrCTnRNZSlC9fHnv27MGUKVMwadIkVK5cGZs2bcL58+dx7do1XLt2DcuWLVM9Pzw8HD7ZR8URERFR/oKCgxGXmMiChgnkD1MsaHTs0QOXzpwx6/xhKTmqtOPQF0N9kZ5fQQNgHiwoDxqjsBEfH49flvyCzIxMg782lZydnR0+/PBDFjbIrLGoYcUYKAr22siR8PTyKtGyzq6ueHnAgFK9vnf16hgyblyJl7extUWT1q3RpHXrEq9DF+MoqYS4WPQdPCrPyaEhJrL5TWAlhpjIFjSBlbRo0wkvkhIRdilEp69tbO3bt8epU6dytWte05iIiIiKJzYhAaP69mVBgwWNPFXw8rKI/GEpOaq049AXfX+RXlBBQ8I8qGbswkZycjIyMzLRpn1LhPx3Ce7lyqLry+2Ncu/DjIwMHD4QjLjnCXjplQ7wrOBh8D4AQOjlMIReCEPj5vXRuEl9o/QhJjoW/+4/kef+iH32HHt3HUZycjKLGmTWWNSwUgwUZOo6dO9T4KRQnxPZwiawEn1OZIsygZXUb9zC4ooaREREpHt9OnRgQYMFDaJS09cX6UUpaEiYB9Vy7o/qvrV10ofi8KtVAz5+1bBt426cOHoGA9/oBXsHw3/GDh7eD9s3B+HIwWC89mYfeHkb/jO2m1dHuLg449TxELi4OCOgneH/5lX08oRHeXej7w8ifWJRwwoxUJA58PAsfPKhj4lsUSewEn1MZIszgdUHa7gmqq7xmqRERGQOKnoY56hV5g8lFjTIkui6sFGcgoaEeVDNFO656OVdEa+92QfbNu7G9s1BRvki3d7BHgPf6IXtm4OwbeNuoxU2pELGqeMhWj8bkinsDyJ9YlHDyjBQkKXR5US2uBNYiS4nsqZQ0FiyZAkyMjIM+rrmjtckJSIiyhvzhxILGmSJdFXYKElBQ8I8qGYK91w0hS/SWdhQy2t/EFkKFjWsCAMFWSpdTGRLOoGV6GIia+yCBqC8JmpGRgZ69u0Kj/LlSryegq7haSiGuqYqr0lKRESUN+YPJRY0yJKVtrBRmoKGhHlQzRTuucjChpopFjY6dC75/YKITAmLGlaCgYIsXWkmsqWdwEpKM5E1hYKGJo/y5VDRy7PEy5vKNTxN4ZqqRERE1oj5Q4kFDbIGJS1s6KKgIWEeVDOFey6ysKFmaoWNwweCDf76RPogN3YHSP8YKMhaSBPZ2JgoBG1di/T0tEKX0dUEViJNZD08K2L3lkBEPX5Y6DKmVtDQFWni9Cw6Fts3ByE9Ld3gfZAmsuUreGDbxt148ijK4H0gIiKyNswfSixokDVp0aYT/Nt3RUjwYZw/fazQ5+uyoCFhHjQtzINqAe1aoG1Hf5w6HoL/Tp43Sh+k/RH3PMEor0+kayxqWDgGCrI2xZnI6noCKynORNZSJ7ASTmSJiIisC/OHEgsaZI2KWtjQR0FDwjxoWpgH1UylsPHSKx2M8tpEusbLT1kwBgr9eXz/PvZv3460lBT41KmD9t27w9nVtcjLZ2VmIjQkBGeOHoVMJkPrzp3RqGVL2NgW/VfyRWIiju7ZgwcRESjr7o5OPXvCu0YNo43DlBTl1GN9TWAlRTn12NInsBKeekxERGQdmD+ULLWgYWr5w1JyVEnHYaoKuxSVPgsaEuZB08I8qGYKl6LS170miQyNZ2pYKAYK/Tm4cyf+WbcOaSkpAICImzexdulSPIqMLNLy6WlpWDF/vqoQIITAf0eOYMX8+UhPK/z0WAB4FBmJtUuX4kFEBAAgIS4OuzZswMGdO402DlNT0BE6+p7ASgo6QsdaJrASHqFDRERk2Zg/lCy1oGGK+cNSclRJxhH9+HGR+2sM+Z2xYYiChoR50LQwD6qZwhkbRJaARQ0LxEChP48iI3H3+nUAwKAxYzBm4kR0eOUVAMCuDRugUCgKXcfOv/4CAJTz9MTITz7BWx9+CM9KlQAAQZs3F7q8QqHArg0bAADtunfH25MnY9CYMQCAu9ev4/H9+wYfR58hQwp9vjHkNZE11ARWktdE1tomsBJOZImIiCwT84eSpRY0TDF/WEqOKuk4ju/fX+jzjC1nYcOQBQ0J86BpYR5UY2GDqPRY1LAwDBT6dWzvXgDKCWz5ihVhZ2+PBs2bo6qvLwDgYfYRP/kRQuB5TAwA4PW334ZjmTJwcnHBgJEjAQBPHz6EEKLAdUhHAFX19UWjli1ha2uL8hUr4rXRowEAB7ZvN/g4HBxMdxKmOZFd9b85Bp3ASjQnsjvWLceOdcutbgIr4USWiIjIsjB/KFlqQQMwzfxhKTmqNOMwB5qFjVX/m2PQgoaEedC0MA+qsbBBVDosalgQBgr9S4iLAwC4eWhfg7Ba9iQ25cWLApfXnKDKZDLVv+VyeZ7PycuLpCQAQI1atbTa3cqVAwCkZp8GXRB9jcNUVaxcBd7VfFQ/N2oRYPA+2Ns7wL9dF9XP/u26WN0EVsKJLBERkWVg/lCy5IIGYNr5w1JyVEnGYS40s5d3NR+DFjQkzIOmhXlQjYUNopJjUcNCMFAYRlUfHwDA9UuXVG1CCJz6918AQIXKlQtcXnOymqwxGdWc/Go+Jy8VvLwAAMEHDmhNeG9dvQoA8K1bt8DlAf2Nw1SdP30MkXduoEbNurCzd8h1TVVDiHr8EPv/3gSPCpXgUaES9v+9SeuaqtaGE1kiIiLzxvyhZOkFDcC084el5KiSjMMcSJecsrN3QI2adRF554bWPTYMhXnQ9DAPqrGwQVQyLGpYAAYKw+ncuzcA4OTBg9izcSOunj+PZfPmqR4v5+lZ6DoCunQBAPy1ZAlOHz6Ms8eOIfCXXwAALdq1K3R5jwoVVP9eNm8eQs+exZ5Nm3Ai+7qq7bp1M/g4LoeEFPp8Y9G8ZmqPAUPyvVmcPmleM7XfkDHoN2RMnjeLszacyBIREZkn5g8layhoAKaZPywlR5V2HKYs5z00egwYkufNw/WNedB0MQ+qsbBBVHwsapg5Bgql9IwMg7yOs6sr+mbfFPtBRASCDxxQPTbq00+LtI4mrVujdqNGAIDLZ87gwqlTAIC6jRujZYcORVqH5mud+vdfPAgPBwD0HTIEzq6uBh/H7eyjm0xNXjeBy+tmcfqU103g8rpZnLXiRNb0ffLJJ5DJZKr/auW4ZAMZTs+ePbFq1Spjd8NqcfsTKTF/KFlLQUNiavnDUnJUScfRd+jQIj3PWPK7KXjOm4frG/Og6WMeVGNhw3wwI5sGW2N3gEqOgUIpLT0dQcHBBns97xo1MHbqVDyKjERyUhI8vbyKdbSMTCZD1z590K5bNzwID4cQAtX8/ODg6FjkdTg4OmLctGl4HhOD6MePUcbZGVV8fAo9VVlf47h24QLOHjP8acQFyWsCK5Emsru3BCJo61q93aAtrwmsRJrIBm1di91bAg1+wzxTIk1kt23cje2bgzDwjV6wdzDsZ4k0kd2+OQjbNu7Ga2/2gZe3ZX85UVQhISHYvXs32mUfOWhjY2PkHlmntWvXYt++fRiS/UUKGRa3P5ES84eStRU0ANPLH5aSo0o6DnsTvjJCfgUNiZTNQoIPa/2sa8yD5oN5UC2gnfJv66njIVo/k2lhRjYNPFPDTDFQKEmBIjYhwaCvK5fLUdXXF3UaNy7x6b8Ojo6oWb8+ajVoUKwJrKZynp6o07gxqvn5FWsiLtHVOKQb45mKgiawEn0foVPQBFbCI3TUeISOacrMzMTVq1fRqVMnuLu7w93dHa5FOIqRdCs2NhaTJ09G3SJc65t0j9ufSIn5Q8kaCxqaTCV/WEqOKu04TE1hBQ2Jvs/YYB40P8yDajxjw7QxI5sOFjXMEAOFkmag6FPE03TJ8hVlAivR10S2KBNYCSeyapzImp7Q0FAoFAo0a9YMZcqUQc+ePXHv3j1jd8vqTJ48GQMHDkSbNm2M3RWrxO1PxPwhsfaCBlFBilrQkOirsME8aL6YB9VY2DBdzMimg0UNM8NAoZQzUFT08DB4H8j0FGcCK9H1RLY4E1iJKU5kQy+HGeV1OZE1LdeuXUPdunURGBiIy5cvw9bWFuPGjTN2t6zK4cOHcejQIczTuAkpGQ63PxHzh4QFDaL8FbegIdF1YYN5UCkjw/AZSleYB9VY2DBNzMimg0UNM8JAocRAQXkpyQRWoquJbEkmsBJTK2yEXggz2sSJE1nTMXz4cISEhKBt27aoXbs2li5digMHDiDBwJf8s1apqal477338Ouvv/KUZiPg9idi/pAwfxDlr6QFDYmuChvMg0rp6WkIPhRUomVNBfOgGgsbpocZ2XSwqGEmGCiUGCgoL6WZwEpKO5EtzQRWYkqFjcbN6xt14sSJrGmqWLEiFAoFHj9+bOyuWIVvv/0WrVq1Qp8+fYzdFavE7U/WjvlDifmDKH+lLWhISlvYYB5UkvZHQlxssV/b1DAPqrGwYdqYkY2HRQ0zwEChxEBBedHFBFZS0omsLiawElMpbDRuUt/oEydOZI1v6tSpWLdunernU6dOQS6Xo1q1akbslfVYt24ddu7cqboB3bp16/DBBx/ggw8+MHbXrAK3P1kz5g8l5g+i/OmqoCEpaWGDeVBJc3906G4ZB2QwD6qxsGE6mJFNB4saJo6BQomBwvqEhRb+h1qXE1hJcSeyupzASoo7kdXXNVNNYeLEiaxxNW3aFF9++SUOHTqE/fv3Y/z48Rg5ciScnJyM3TWrcPz4cVy5cgUXL17ExYsX0a9fP8yaNQuzZs0ydtesArc/WSvmDyXmD6L86bqgISluYYN5UCnn/vDwtJzPK+ZBNVPI58SMbEostqhx5coVtGrVCuXKlcPUqVMhhDB2l4rtfFgYAwWMFyiEEKV+3ygUilKtQwgBhUJRqj6YwjhKIuxSSIETWX1MYCVFncjqYwIrKepEVt/XTDWFiRMnssbz1ltv4c0338SgQYMwdOhQ9OzZE7/88ouxu2U1qlatCh8fH9V/Li4u8PT0hKenp7G7ZhW4/SknS8gXhWFBQ8maCxqWkj84Dv3RV0FDUtTCBvOgkr73hylgHlQzhXxu7ZiRTYetsTugD2lpaXj11VfRo0cPbNiwAZ988glWrVqFMWPGGLtrxRISFsZAYcRAsX3NGtW/B4wYgYre3kVaTgiBy2fO4L8jR1Rt5Tw90f+tt2DvULRJTnpaGoI2bcLTR49Ubc3btoV/x46QyWRFWkf048daYwAMP47SqN/UHyHBhwEg1yRVnxNYiTSR3b0lEEFb1+aapOpzAiuRJrJBW9di95bAXJNUQ10zNaCd8jPo1PEQrZ8NSZrIbtu4G9s3B2HgG71g72DYzyRpIrt9cxC2bdyN197sAy9vy/+S44cffsAPP/xg7G4QgFWrVhm7C1aN29+6WUq+KAgLGkqmUtA4HxZm8Nc8sns3YmNiVD+bY/6wlByli3Hog6G+QJcyHvNg0fKgJRc0JMyDaqaQz60dM7JpsMiiRlBQEOLj47FgwQI4OTlh9uzZ+PDDD80udPjXr89AYQKBAgB2BAZi4KhRqODlVehzj+zZg1tXrmi1PY+JwapFi/DO5MmwsS341y4rMxOrFi3K1X7h1CkkJyWhc+/ehfYh6tEj7AgMzNVuyHGUVv3GLeDs4pprImuICawkv4msISawkvwmsjmvmXpk7w699QEwjYmTqU5k5XLdnvQYHx+P5ORkna7T0jk5OcHNzU0n6+L2Lz5uf+PS5fan/FlKvsgPCxpKppI/jp0/jxAjFDU0CxqA+eUPS8lRuhiHPmRkpBv0C/T8ChvMg7nzoKUXNCSmmgdZ2Cgc5/jmxVzyhUUWNS5duoQ2bdqormfWpEkTXLt2zci9Kr4W9esb5XUZKJReGzUKnl5euHvjBg7u2IHtq1dj3LRpBS6TlpqqmsC+9eGHcHJxgRACW1auxPOYGISeO4dmAQEFruPKuXMAAE8vLwwYMQJyuRwpL14g8JdfcCM0FG1eegkOjo4FrkOaiHcfMAB+desCgMHHoQs5J7LSvw0xgZXknMj6t+uC/X9vMsgEVpJzIvtKv8EIOXlENYHV9Zfq+TGFiZMpTmS7dG+vs3XHx8djyZIlyMjI0Nk6rYGdnR0+/PDDUk+84uPj8cv//ofMrCwd9cw66HL78/1ffLra/lQwS8kXeWFBQ8kU8geg3h/+9esbvLDhXr48Xn/7bbPNH5aSo4oyDmMIPhSExIQ4g36BzjyoVFgetIaChsQU8yALGwXjHN/8mEu+sMiiRkJCAnx9fVU/y2Qy2NjY4Pnz5yhXrpzWc9PS0pCWpr4+Ynx8PAAgNjYWmZmZAAC5XA65XA6FQqF1PUupPSsrS+s6mfm129jYQCaTqdar2Q4AWdlfosTFxSEtLQ13799HQmIiFDmuwWkjlyuvranRLst+3fzaFTmuByqTySCXyXK1P4uLw/7Tp1HO1RX+9erh7oMHyjHJZJDJZMprgmr0RWrPynGdT3n2KbE5+55fu+aYMjIycOjsWcQlJuKVNm2Q9OIFwu7eLXBMz+LjkZaWhsTERJQpU6ZU+ykhIQGpqam4Hx6OhOz3Q1paGoQQuHvjBnKSTv8VQuDRvXtIS0uDX/36iHr8WHUd1katW+Pg9u04vm8f3D088rw+q7T/ju3bBwCo36wZIm/dgkwuh1AoUK1mTdy+dg1njx1DFR8f1f7Iqy+a7+m7N24o97dcrmq/c/06ZNn7Tuq7pkf37iE1NRU1GzTAk4fqa3c2adMG+7ZswfF9+1DW3V29zeLikJqairi4ODhkn1It/d7l93uTX3t89r68H3kHCQlxKFe+EmrUboDgf/cCAOo0agb38hUQfks5LoXQ/vJRln2rIAFFkdrlMhvl/tBql0Euk2u1N23dHsGHghB59xbKupdDvab+eBB5N/vZMshkcgihgIDm75+yXSEUgFa7PLvv+bXnPaZ6Tf1x+sg+bFu7HADQvltvvEhKQtzzaKSlpam2v62tLYQQqs+UgvZHznbp8+dexH0kJSZBKITWmDw9y6Fm3Ro4cjAYUU+jUb9BbdV68ntPFvRezfney69d+v2Q2v0DmuL4kf+wevlGdOwaAFsb2wKfr9ofclmuMZWk7zKZDE2a10Pw0bPY8/dBZGVlISEhAU5OTlrbHcj9GS/Jaz9FRUUhMTERbVs3QVk3FwDKazEf/Pc/NGlYC5UrV9D6DJZlj0t7RLnbHz+ORujV2+j+UgBs5Tb5Pl/7E17ZDijfpRcuXgcANG9WT6tdk7yAvmQqsnDw3//QOHscRe27Zvvjx9G4nD0OqZiXEJ+EU2cu4/Hjx1rbsqh/czXbnzx5ghc5jiCS3kNNatVH5Ype6j7KZBBC4EVKMs5evYj0jAxln7Ofr3lpCBlksLO1RetGzeFUxkmjXfm8B08f48qd67leM+flJSqWK49mdRurd4z0fIXApZtXEPX8WZ5911xP45r1UKVS5ezPN7XklGScuXIB6RkZudefvR65TA57Ozu0atgMzmWcIJPJEJeYgNOh5/Do0SNkZWWVam709OlTJCUloWXjNnB1kSbQOd9leb/7ZNmf2cjxrpH2k3Hac14DPb/fnJKPKTEpAedCT+Px48eqz6vi/M3VxRw2ISEB1qA4+QIwvYwh/X2/k50vAOV8PPTWLVy8eRPN6tSBl4cHboSH6yVjqNrzyBIx2fnDXSN/6CNjFDSmjIwMHDpzBs+z80dCUhISkpJKPKaS5ibN/eHu4pJrfg3kPX8o6jxP9Zo53nvS+6NG7dq4d+eO6rma+aOqr2++c6KsrCyt93v4zZuqf2vmD+lvd17zvAcREaocJeUPuVyOxgEBOLBtm1b+yHueBxzduxcymQz1mzVDxK1bqj5qjsO7Rg2tvmuOSQiB1NRU1c+a2U9ql3KU5jbQHJNmHnz66JGqXcqDx/buhVu5cgXOdVV5sHlz3LtzByL7vaQ5Dhc3N635v+Z+Len3EPm1S++PRw8i0aF7HyQlJSDplvTZnzs3abfnnY+Kk5s8PL1Qo3Z9rTxYrnwlRNy+abAsCCjz4ImDexB59xbcynmo8qChsqCAQisPCiHy2B/KMT2P1c6Hpf2MyNmu+R6Ly/4u4H7kAyTEa88J9JUFAWUePHb4NFYv34gOXVrDztauwOfrOgsqFApVHly7ais6dglAec/cv9vFGVNx22WQoUIFD9Sso53PC+t77LPnWu+P0n5GSPL7+xQdHY3ExEQEtGqCMyGhaNywFrwrVyh2FpRBhoePo1S5Vi6XF/r8vPLt+QthgEyG5s3qabUDRcu3CoUCBw6dRpNGtVG5coUi912z/fHjaFy+cguvdG8LuUxWrG0gjUnK5y2yx1G0hKEeU1b29ww590d8fBJOnw3Fo0ePoFAoSv0ZUVB7fu+9Fy9eKPteyD2pZMIC73A3bdo0ZGRkYMGCBaq2atWq4fTp06hSRbuC/c0332DmzJmG7iIRERERkcWIj49H2bJljd0NvSlOvgCYMYiIiIiISuP+/fuoWrVqvo9b5JkaHh4euJLjGpaJiYmwz+M05unTp2PSpEmqnxUKBWJjY1G+fHmj3oDLFCUkJKBatWq4f/++RYdWU8Xtb1zc/sbF7W9c3P7Gxe1vXNz+BZOOj3J1dTVyT/SrOPkCYMYwJP6OUkH4/qCC8P1BBeH7gwrC94f+CCGQmJgIb2/vAp9nkUWNVq1aYdmyZaqfw8PDkZaWBg8Pj1zPdXBw0DqdFwDcNS6rQ7mVLVuWv7BGxO1vXNz+xsXtb1zc/sbF7W9c3P7WrTj5AmDGMAb+jlJB+P6ggvD9QQXh+4MKwveHfhTlfh6GubusgXXq1AkJCQn4888/AQCzZ89G9+7dVdd9IyIiIiIiKirmCyIiIiIi02GRZ2rY2tpi+fLlGDp0KKZOnQq5XI4jR44Yu1tERERERGSGmC+IiIiIiEyHRRY1AKBfv364c+cOzp07hzZt2qB8+fLG7pLZc3BwwIwZM3KdSk+Gwe1vXNz+xsXtb1zc/sbF7W9c3P4kYb4wTfwdpYLw/UEF4fuDCsL3BxWE7w/jkwnp7n5EREREREREREREREQmzCLvqUFERERERERERERERJaHRQ0iIiIiIiIiIiIiIjILLGoQEREREREREREREZFZYFGDiIiMjrd3IiIiIiIiIkNhBiUybyxqWLG0tDQ8fPjQ2N0gIisWHR0NAJDJZJxUktVRKBSqf/P9T0SkxIxCRET6xAxKZBlY1LBiY8eOxccff4z79+8buyuUjX9QjUdz23M/GMb69esxePBg/P333wA4qSTrkpWVBblcjsePHyMxMREymUyryEFEZK2YUag4OHek/DDfUV6YQYksB4saVmzu3LlITEzEzJkzERERYezuWKWsrCwAyiMFsrKyIJPJjNwj6yOEgBACMpkMmzZtwsGDB7kfDGD16tX47LPP0KFDB2zYsAFHjx4FwEmlsTD0GZ6NjQ2uXbsGf39/tGzZEklJSZDL5SxsGBHf+0SmgRmFCsL8RIVhvqP8MINSUTEfmwcWNaxUeHg4HB0dsXr1aly+fBkLFy7EvXv3+GWKASkUCtjY2ODcuXMYOXIkMjMzsXfvXpw9e9bYXbNoKSkp2LJlC+Li4gBAa8I7ZMgQxMbGGreDVmDNmjWYPXs2QkJC4O7ujrNnz2L58uX4559/AIChw4AY+gwrJSUFmzdvBgDcvn0bAwcOxJdffommTZuiefPmSE5OZmHDQDIzMwEAd+7cwdWrVwHws4fIFDCjUEGYnygvzHdUFMygVBTMx+aFRQ0rkpqairNnz+LFixf44Ycf0Lp1a0yYMAFDhgzBhQsX8PXXXyMlJQW3bt3Cli1bjN1di/XgwQOEhYVBLpfj/Pnz6NWrF8aPH4/g4GD069cPKSkpxu6iRVuyZAk++ugj7Nq1C3FxcZDL5di4cSOGDh2KDRs2YPDgwcbuokXbvXs3Ro8ejRs3bmDfvn3YunUrNm7ciIEDB2LNmjU4ffq0sbto0Rj6jGvZsmX46quv8MMPP6Bnz574/PPP8f777yMwMBB+fn5o2rQpEhISIJdzeqZPWVlZsLW1xfnz59G1a1c4OTlh+/btmDRpkrG7RmSVmFGoMMxPVBDmOyoMMyjlh/nYzAmyCllZWWLu3Lni008/FUII8fz5c/Hs2TNx9OhRIYQQaWlp4qWXXhIjRowQtWvXFnPnzjViby3bhAkTRNOmTcW1a9dE5cqVxe7du8XJkyeFnZ2d2LFjhxBCCIVCYeReWrbp06cLf39/sWfPHrFy5Uohl8vFxo0bhRDc9vp069Yt8eTJE2Frayv69esnunTpIp4+fSqEEGLp0qWibt26Yt26dWLXrl1G7qnl+vHHH0WlSpVEYGCgeP78uRBCiA0bNmj9DpB+ffjhh8LJyUmMGTNG1ZaZmSmEEKJdu3aiRYsWIjU11Vjds2gRERHim2++EUIIcfHiRVG5cmWxY8cOceLECSGTycTu3buN3EMi68OMQkXB/ESFYb6j/DCDUkGYj82brbGLKqRfIrvK+Pz5c1y/fh1dunRBVlYW3N3doVAocP/+fXz66afw8/PD5MmTMXHiRJQrVw7vvvuusbtusRYuXIh27dqhefPm2LJlC8qXL48OHTpg8+bN6N+/Px4/fgwvLy9jd9MiZWVlwcbGBrNnz0bZsmXx0UcfISIiAitXrsTgwYOhUCjyPEJa+j2iklu/fj2GDx+OoKAg3L17Fx06dMBbb72FihUr4u+//8bMmTOxc+dOJCUl4ZdfflEdPS3hPtCNKVOmIDY2Fj///DPKly+PJ0+eYOzYsaob5nE764f02XP79m0cPHgQ3bp1w/3797FmzRr07NkTFStWxMOHD5GUlIQRI0bAwcHB2F22SJmZmZg3bx5kMhl+//13LFu2DO7u7ujatSu2bduG3r17A+DnDZEhMKNQcTA/UX6Y76ggzKBUGOZj88brG1g46Zfvu+++w6pVq7B7927Y2Nhgw4YN+PHHH2Fvb4/k5GScOnUKsbGxePXVV5GUlITPPvuMN+bTMemmdpcuXcKdO3fQp08fpKSkoEOHDti0aRMGDBiAsLAw1KhRA9u2bTNyby2LtO1tbGyQkZEBAGjQoAEiIiLg6+uLcuXKqU5Vzol/xEpv1apV+O677/D+++/j8OHDqFatGvbs2YPFixfjzTffxKefforq1asjPDwcy5cvx/vvvw8nJyc8e/YMoaGhUCgU3Ac6IP0ezJ49G4MGDcJHH32EsWPHaoW+vLaz4I3RSkUIobopeJ8+ffDZZ5/h77//RocOHbBs2TKcPn0aV65cQc+ePfHWW29hypQpquVId9LS0lCzZk189dVXmDVrFv744w9UqFABXbp0Uf0NvnbtGk6ePMnPGyIDYEahomB+ovww31FhmEGpMMzH5o9FDSuwY8cO/PPPP7hz5w6ePXuGrl27Yty4cfDz80PVqlWxbNkyBAYG4ubNm2jatCkOHjyI8PBwTJw4EXfv3jV29y2GdFO7vn374ptvvsHNmzcxbNgw7N+/HwMHDsSlS5fQt29fZGZmokyZMsburkWIi4tDamoqbGxsACj/+NjZ2WH9+vUYOHAgdu7cidGjR2PWrFn4+++/kZCQoLX8jBkzVF8wUvEpFArExcVh3Lhx6NOnD5YsWYIjR47gn3/+QcOGDXH27Fn8999/6NevH86cOYOEhARcuXIFU6dORYcOHdChQwf89NNPuHTpkrGHYhEY+gxHc6Irk8nw8OFDDB48GMOHD8fbb78NQPn5MmDAAHz//ffo2bMnBg8ejKlTpwIAQ5SOZWVlwcHBAaGhofj5559RuXJl/Pfff+jQoQM2bNiAAQMG4Pr162jVqhV++eUXY3eXyGowo1BhmJ8oJ+Y7KgwzKBUV87EFMOzVrsiQsrKyhBBCpKeniydPngghhAgMDBR2dnYiICBAhISEiOrVq4u1a9eKTp06ib59+4q0tDQhhBD3798XXbp0EQ8ePDBa/y1NVFSUcHV1FVu3bhU3b94UcrlctG/fXgghxNWrV4WXl5eYO3euGDNmjDhw4IBqOWk/UvEkJCSIypUri0WLFmm1b9y4UdjY2Ij169er2qZNmyZatmwpVq9eLeLj44UQQsycOVO4urqKixcvGrTfliQpKUkIIURwcLDw8PAQmzdvFgcOHBANGjQQu3fvFsnJyeLRo0eifPny4scff1Qts2XLFnHy5Emxb98+cebMGWMOwWJkZGSorie8bt06IZfLxT///CO+/fbbXO99yddffy0mTZpkjO6aNc3rNv/xxx8iLS1NnD17VgwaNEiMGTNG3Lp1S/V4TEyMqFixovD19RV///23SEhIMEaXrcLp06dF5cqVxfHjx8XUqVOFjY2N2LRpkxBCiAsXLogqVaoIuVwu3n77bSP3lMjyMaNQUTE/kSbmOyoKZlAqCuZjy8CihoWSbjp64cIF8eWXXwohhNixY4eQyWTip59+Es7OzuLNN98U169fF1evXhUff/yxiI6OFkII1Q1KpXWQ7iQlJYlTp04Je3t78eOPP4qRI0eK6dOni6pVq4qvv/5aCCFEt27dxOrVq8WpU6dEVFSUkXtsnhITE0Xz5s3Ft99+K4QQquB75MgRIZPJVF9kpaenq5aRJr6bNm0SX3zxhShfvrw4f/684TtvIf744w/xzjvviOTkZCGEECdPnhRly5YV3333nfjf//4n6tWrJ1566SXx008/iaioKOHp6Sl++eUXERkZKYYPH54rjPIGf8Ujba/NmzervggSgqFP3zTfpzNmzBB+fn7ixYsXQgghDh48KEaNGiXeeOMNER8fL+Li4kSjRo3Ezz//LObPny86deokVq9eLWJjY43VfYuVkZEhhgwZItavXy9Onz4t5HK5CAgIEHv37hUXLlwQlStXFvPmzRNbt24VEyZMMHZ3iSwaMwoVF/MTCcF8R0XDDEr5YT62TCxqWLBLly6JsmXLit9++00IIcSJEyfEqVOnhBBCtG3bVshkMjFp0iRVQBBCOyTwA1z3Dhw4IGQymdi1a5cQQvlhKZPJtI42efXVV0XTpk2Fm5ub2LBhA/dDMSUkJIhWrVqJcuXKCSGUVfd3331XrFq1Suzbt08cPXpUCKF+f2u+52fMmCHKli0rKlasKC5cuGDwvluKX3/9VcjlctXnjbSN//vvP1GuXDnx119/iaysLHHnzh3h6+srDh06JG7cuCE8PDxEpUqVxOLFi43ZfYuwZMkS8fLLL4tWrVqJxMREIYQQu3btYugzkC+++EIEBASImJgYIYT68+bgwYNizJgxolevXsLX11fMmTNHtczMmTNFrVq1xKpVq/i5rwN5HaUbFBQkHBwcxI4dO8T8+fNFgwYNRKVKlcTMmTOFEMq/F0OHDhW///67au5ERLrHjELFwfxEzHdUFMygVBDmY8vEooYFSU1NFTt27BBCCHHjxg1RqVIl8fPPP6se1wz4K1euFBMmTBABAQFiyJAhWpVK0o2cX6hs2LBBbNy4URw/flzVlpaWJoKDg0VmZqZYuXKlaNasmahbt66YPHmyuHTpkqG7bPbi4+NFvXr1xMyZM0Xbtm1F8+bNxfjx48WQIUPE9OnTRWBgoOq5mvtHmvDMmzdPeHl5iWvXrhm875Zi6dKlwtHRUQQHBwsh1NtZ+v/JkyeFm5ubWLp0qRBCiGHDhoklS5YIIZRHbTo6Ooo//vjDCD23DCkpKSImJka899574pVXXhFRUVGqydfRo0fF7t27hRAMfbqWlZUlzp07J8LCwsT//d//iXbt2omUlBRx7NgxERkZqfXcEydOiFdffVX88ssvQojcZ3fcvn3boH23RBkZGUIIISIjI1VfdEifL5s3bxZCKC9h4+3tLRYsWCCEEOLFixeiT58+wtvbW3h6eooVK1ZwbkSkI8woVFTMT5QT8x0VBTMo5Yf52LKxqGFBfvzxRzFw4EAxf/584eXlJb755hshhPKDXKFQiPT0dNWH+vz588Xrr78u0tPTRYcOHUTv3r15LW8dSE1NFXv37lX9LG3vBQsWCBcXF3Hu3DkhhPIDU/OLrEuXLokJEyaI6dOni+TkZJ5WXwIxMTFi2rRpYuHChUII5ZEar776qggMDBRRUVHizz//FFOnThXr1q1TbV/NfTBr1izh4eHB6nsprF69Wtjb26tOyZSuU5meni4UCoXqi8ZTp04JNzc38e2334odO3aIVatWqR47efKk0fpvzqT38qZNm4SNjY1o0KCBEEKId999V8hkMjF//nzVc3N+vjD0lY5CoRCpqali0KBBwtnZWXTt2lWkpqaKr7/+Wnh6eoovvvhCCCHEzp07xR9//CGysrK0rs+alZXFz3wd0fwyIzQ0VFSrVk3Y2NiIHTt2iJiYGNV7W/qSNDo6WmRmZor58+eLRo0aifbt24s1a9bkKkQRUekwo1BBmJ8oP8x3VBTMoJQX5mPrwKKGhfnoo49EmTJlhJOTk/jggw/EgQMHxLNnz3I978GDB6rTuhMSEkTHjh3F/fv3jdBjyzJnzhzRtWtXsW3bNlXbb7/9JsqVKyf27dsnhMj/lHnpesEFPYfylpiYKOrVqyemTZsmhFDeFKxZs2bizz//FKGhoWL8+PHi1KlTYtWqVWLixIni999/1/rya9q0acLBwYHV91JYtWqVkMlkYsSIEUII9ZHS+fnvv/+Em5ub+Omnn/J8nL8DJTNp0iTh5+cn/P39xYABA0TdunXFTz/9JGrWrCm+++471fNy3vSMoa9kNN+n77//vujXr5/IzMwU3333nWjTpo2YNWuWWLt2rfjqq69E3bp1xRtvvCHmzp2rOrW5sN8TKrqUlBQxc+ZMERERIcLDw0WdOnXEtm3bxMqVK4W9vb34+++/81wuKytLrF+/XixfvtzAPSayLswolB/mJ8oL8x0VBTMoFYb52LLZgsxeVlYWbGxsEBERgS1btiA1NRVNmjTB3bt3MWnSJDRs2BAvXryAn58f7O3t8eLFC0RFRWHx4sVwcHCAg4MDjh07ZuxhWITRo0fj5s2b2L59O7y9vXHx4kVMnz4dGzZswCuvvAKFQgG5XJ7nsp6engAAIQRkMpkhu23WEhMT0aVLF9StWxdz5swBADRv3hwNGjTA9u3bMXr0aDx9+hQ//vgj1q9fjzJlyuD27dtITExE2bJlAQC2trYIDg5Gs2bNjDgS8/Xrr79i0qRJGD16NFJTUwEAERERmDNnDlJSUpCeng4bGxvY2dlh0qRJaNq0KVq3bo19+/bhpZdeQnp6OqZPn661Tv4OFN/EiRMRHByMoKAgjBkzBnv37sXnn3+Ovn374vjx41i0aBH69u2Lv/76C02aNMHw4cMhl8vx+eefY9GiRTh9+jR/B4pB87N6+vTpuHDhAo4cOYLvv/8ee/bswTfffAMAWL58OZ49e4Z//vkHt27dwsaNG7Fw4UJMnDgRdnZ2RhyBZTl16hT+/fdf3L59G8eOHcM333yDgQMHAgCioqIwaNAgrF+/HoMGDVItI4SAXC7HkCFDtNr4+UOkG8woVBTMT5QT8x0VBTMoFYb52PLlPTsgsyGFhcuXLyMgIACvvvoqGjZsiLp166Jz585466238O2336JTp07w9PREVFQUbGxs0L9/f1SuXBmAchIohDDySMxfVlYWKlWqhNmzZ8Pe3h6fffYZpkyZojUhz29bKxQK1b/5h7To4uPj8dJLL8HR0RFnz55FaGgoAODJkyf43//+B19fXwwZMgTbtm1DbGwsPvzwQ7Rq1Qrx8fEYOXIkPvvsM7x48QKzZs1Cy5YtjTwa8/T7779j4sSJOH36NFauXIm///4bc+bMgbe3N8qUKYMKFSrAz88PTk5OiIqKgp2dHeRyObKyshAQEIDdu3fj5s2bxh6G2UpNTYVCocC9e/fw5MkT7Nq1CwsWLEBMTAxee+01bNq0CWPHjkVKSgpee+01jBkzBlu2bMGbb74JuVyOhIQE2NvbM/SVgPRZPWvWLBw/fhxHjx7FokWLsGfPHvzf//0fAOC3335DfHw8du7cibJly6JXr17o06cPbty4gb/++suY3bcoCoUCXbt2xZAhQ7Bjxw7Url0bvXr1AgCkp6dj2rRpmDx5MsaOHYv79++rlsvr7y3/BhPpBjMKFQXzE+XEfEdFwQxK+WE+tjJGOT+EdOrixYvCzc1N/O9//xNCCDF9+nQxaNAg8dlnn4lhw4aJrVu3CiGU1wn89NNPjdhTy6d57T07Ozvh4+Mj5s6dK27duiXCw8O1npuVlSXOnj3LGyCWwsOHD8Vvv/0mhBDi66+/Fn/88YdYv3698PX1Fe+88454+vSp+Pjjj8WwYcPEo0ePxGuvvSZ++OEHMW3aNDF8+HDx008/iSdPnhh5FOZr7ty5okyZMqprkD5//lz4+fkJuVwuZs2aVejyOa9dydN9i++jjz4S9erVU/185MgRIZPJxK+//iqOHz8uGjZsKCpUqCCuXLkixo8fL9zc3MS8efO0LvnBSyCV3P/93/+J6tWri7i4OPH555+LsmXLiiNHjoigoCDRv39/0a1bN5GcnCzmzJkjatasqfq8Gj9+vPjggw+47XVA+hy5du2aqFevnvjkk0/E0KFDxYQJE0RISIgQQoh79+4Jf39/MW/ePGN2lcjqMKNQUTA/kSbmOyoMMygVhPnYusiE4OEv5iwjIwPDhg1Dx44d8cknnwAANm/ejH/++Qd//PEHZs+ejadPn8LDwwN//PEHFixYgFGjRhm515bt999/xxdffIElS5Zg7ty5uHTpEt58802cPHkS/v7+ePbsGZKSkpCRkYGIiAhs374dL730krG7bfbWrFmDuXPn4unTp/jpp5/w5MkTREZGYsaMGZg2bRqEEHjw4AEmTJiAfv36Gbu7Zu/Zs2fo2LEjFi9ejO7duyMjIwN2dnYYPnw4KleujOPHj8Pf3x9LliwBAGRmZsLWllc81IeXX34ZKSkpOHHiBB48eICPP/4Yhw4dQps2bWBjY4PevXvjp59+gqenJ3744Qds374ddevWxSeffJLv5RyoaI4fP44GDRqgfPnyWLFiBSZMmIClS5di69atePHiBXbu3ImlS5fi5MmTGD16NJYsWYLvv/8eBw4cwIABA1C/fn1jD8GsiezLjYSFhWHgwIGYPHky3n33XQQFBeH3339Ho0aN0KlTJ0yZMgUjR47ElClTAKDAS5kQkW4wo1BxMD9RXpjvKCdmUCoK5mPrwaKGBUhMTISrq6vq59TUVDRt2hSbN29GkyZNMGLECGzduhXvvfceFi5caMSeWr4lS5bgq6++wrp169CzZ09s3rwZQUFBmDNnDsLDw5GUlIRLly4hMzMTXl5eqFatGrp06cJTpnXk5Zdfxr1793Dy5Ek8ffoUf/31F2JjY/H555+jcuXK6N+/Pzp27Ki6NAyVzosXL+Ds7AyFQgGZTAaZTIY33ngDrVq1Qs+ePTF06FB0794dP//8MwB+kahrmttzwIABCA0NRVxcHB4+fIjvvvsOs2fPRnh4OFauXIljx47hwIEDsLW1xejRoxEcHIwLFy7AxcXFyKOwDGlpaXBwcEB0dDS+/PJL3L59G3///TeWLFmCCxcuYP369Rg/fjwcHBzw888/qwIYlV5cXBx69eqFBg0aYMWKFar2//77DzNmzEBISAhGjhyJBQsWAFBfEoeI9I8ZhYqC+YkKwnxHOTGDUn6Yj60Pf7MtgPRLJ4RAZmYm7O3tUbFiRcTHx+O///7D/v370adPH9y/fx9r167ltWn1ZO3atfjiiy+wceNG9OzZEwDg5eWFw4cPIyUlBQEBAejWrRsmTZqEzz77DCNHjkTXrl05IdcB6T1dq1Yt3Lp1C99//z0yMzMxffp0pKWlYe7cubCzs4O9vT3KlStn5N6at40bN+L69esAAGdnZwCAXC5HZmam6jmOjo5o0qQJVq9ejcOHD+PTTz9VPS8rK8vwnbZQcrlcdT3pd955B+Hh4ShfvjwcHR3x3XffYenSpejcuTMWLlyId955BxcuXEB6ejrc3d0xY8YMTth0yN7eHgBw5MgRnD59GoGBgbh06RIWL16Mn3/+GR9//DEUCoUqXPFLdd2xtbVFjx49cPHiRWzfvl3VXqdOHVy/fh2VK1eGs7Mzrly5AoDbnsiQmFGoMMxPlB/mO9LEDEpFwXxsfVjUsADSpE4mk8HW1hZyuRx+fn44deoU/u///g+LFi3C5s2bUadOHXTq1ImTQD1p3749Dh06hJdffln1R7Njx47w9fXF48ePAWjf0I7BTXek9/Trr7+OwYMHIy0tDQsXLsTOnTtx7NgxVKlSBWfPnkVWVhY6depk5N6arz///BNDhw5Fenp6rsek03qfP3+umlz6+/tj5cqVOHr0qGpSyS8UdUsulyM5ORl79uzBsWPH0KBBAwQEBAAAHBwcYGtri9OnT2PXrl3Yvn077O3tsWjRIgwbNszIPbcsMpkMqampOHjwIFq3bg07Ozu0bNkSffr0weDBg5GRkYE//vgDgPKzn0eL6Y6LiwsmT56MQYMGYcaMGTh27BhSUlLQsWNHTJkyBStWrMDVq1excuVKnDt3ztjdJbIqzChUGOYnyg/zHUmYQak4mI+tCy8/ZWGk60uPGDECTZo0wdSpU1WP8XqChiHtAyEEFAoFGjVqhC+//BLDhw83dtcsXnp6OuRyOWxtbfHVV19h06ZNmDBhAt5//30A/B0ojRUrVuDzzz/H7t270bp163wv4dKlSxd8+OGHeOONN1RtwcHBeOWVV7BkyRKMHj3agL22HqmpqXB0dAQA9OjRAwqFAgcOHFB9Hj1//hxOTk5wcHAwck8tW3h4OKZPn46KFSti0qRJqF69Og4fPoxu3boB4Onv+pSUlIRffvkFK1euRFxcHKZMmYLPPvsMABAUFIRvv/0WAQEBmDNnDn8PiIyAGYUKwvxE+WG+s27MoFRSzMfWgZ/+FigtLQ1RUVFa1+sWQvCPvYFoHpUm/cHlkWeGIV0CBgC+/fZbCCFQoUIFVRuP0CiZ9evXY+rUqQgKCkLr1q2RmZmpek9LE8usrCxERkYiKipKa5sDQLt27XD27Fk0aNDAGN23Co6OjqovzPft24du3bqhY8eOOH78OADAzc2NX6YbgK+vL+bMmYPp06dj8eLFePvtt1UFDZ6hoV8uLi744IMPkJ6ejqpVq+Ltt99WPdarVy/Vl2QMLkTGw4xC+WF+ovww31kvZlAqDeZj68AzNSzU9evXkZKSgubNmxu7K1bt0aNH6Ny5MzZs2ICWLVsauztWQ6q+U+mtWLECU6dORe/evTFmzBh069ZNdUTUtm3bMGfOHBw7dgyOjo5IS0tDSkoKnJyctAKIJh6prl+a27dv3764c+cOwsLCjNwr6xMREYFx48ahZcuW+Oabb/hFugFp3oRd8waSRGQamFGoMMxPlBfmO+vCDEq6wnxs2VjUINKjNWvWYPTo0YiMjES1atWM3R2rojnx5SS4ZFasWIFp06bh4MGDUCgU8Pf3x59//olRo0Zhy5YtGD9+PNzc3FCjRg3s3bs330kkGZbmxG3AgAFYuHAhfH19jdwr6xMREQEbGxt+9hMRERUD8xPlh/nOOjCDkq4xH1suliqJ9EShUMDR0RHTpk1D1apVjd0dq6M5yeWEt/hWrFiBr7/+Gv/88w+aNWuGFi1a4I8//sC4ceMwffp0vPfee1i7di1CQ0Ph5OSE0aNHIyMjA4D2DR3J8ORyuWof7NixgxM2I/Hx8eGXMURERMXA/EQFYb6zfMygpA/Mx5aLZ2oQ6RmPIiFzExkZCV9fX8yaNQtffvklUlNTYWtrC1tbW4wbNw4rV67E3LlzMXnyZKSnpyM4OBjz589HrVq1MH/+fF7bloiIiIhKjPmJyPowgxJRcfFMDSI944SczE2NGjXwyy+/YPbs2di+fTscHR1ha2uLjRs3Yvv27Rg1ahT279+PsLAw2NvbIyAgAEOGDMGVK1cQGBho7O4TERERkRljfiKyPsygRFRcLGoQEREAqE7dBYAPPvgAixcvxhtvvIGdO3fiyZMnGDp0KAIDA7Fs2TL4+vri+PHjAAAnJye8/vrruHjxIm7fvm2s7hMREREREZEZYQYlopKyNXYHiIjI+H799VckJibis88+A6A87X/s2LHIysrC66+/jn///RfJyclwdHQEANStW1d1fVMA2LdvH1xcXODv72+0MRAREREREZF5YAYlotLgmRpERISrV69i9erVAIDr16/jiy++QHJyMt577z0sXLgQL730Es6ePat6/qeffgoAOHr0KADg5Zdfxo4dO9C1a1fDd56IiIiIiIjMCjMoEZUGz9QgIiK88soruHPnDsLCwtC1a1c4OTlBLpdj5syZ+Oijj5CSkoI33ngDe/bsQYsWLZCWloakpCTcuXMHnTt3hpOTE5o1a2bsYRAREREREZEZYAYlotJgUYOIyEodOnQId+7cQbNmzdCvXz988MEHaN26NRYtWoS2bdsiLi4OL168gJubG6ZMmYKIiAhMmDABf/31F6pXrw4nJyc8e/YMgPJUYd7UkYiIiIiIiPLDDEpEusLLTxERWRkhBJKSkhASEoIvvvgCly5dwtWrV5GamoqgoCB07doVBw8eRMOGDeHm5oZ79+4hIyMD7733HlxcXLBlyxZcu3YNNWvWhKurq7GHQ0RERERERCaMGZSIdI1FDSIiKyOTyeDi4oJp06YhJiYG3t7e6N69O5YtW4aKFSuiVq1aCAsLg5ubG+7evYvatWtj69ataNKkCRo1aoQVK1agRo0aCAwMxFtvvaVaJxEREREREVFOzKBEpGssahARWZEHDx5gzpw56NGjB27evIm7d+/inXfewfLly1GjRg20atUKADB06FDcv38f/v7++OyzzzB06FAAwMSJE2FjY4NNmzbB0dERLi4uxhwOERERERERmTBmUCLSB5kQQhi7E0REZDiLFi1Ceno6Hj9+jLVr12L58uWoXLkyXnnlFUyaNAn37t3DxIkT0bVrV7z99tv44YcfAAAKhQJCCJw+fRp169aFp6enkUdCREREREREpo4ZlIh0jUUNIiIroXkjtSdPnsDf3x/Lly+Hq6srevfujbVr16JPnz6oV68ewsPDMWXKFMyePRtCCAghIJfz5D4iIiIiIiIqGmZQItIXW2N3gIiIDEMmk0EIgYyMDLz22mtwc3ND165dUadOHfz555/o27cvLl++jIiICNSrVw/Ozs6q5YiIiIiIiIiKgxmUiPSFJU8iIisik8lgb2+PpUuXolKlSoiMjMSpU6fw2muv4caNG+jcuTM++eQT/P777zh//jxmzpypWo6IiIiIiIioOJhBiUgfeKYGEZEVatasGTZs2ACZTIYKFSogPDwcAQEB+PDDD/H9998jKysLkZGRqFKlirG7SkRERERERGaOGZSIdIlnahARWamKFSuiQoUKCAsLQ82aNfHpp5/i+++/BwDs3r0bDg4O6NWrl5F7SURERERERJaAGZSIdIU3CicisnIXLlzAzp078c033wAAFAoFUlNTERERgerVq8PFxcW4HSQiIiIiIiKLwQxKRKXFogYREalkZWXBxsbG2N0gIiIiIiIiK8AMSkQlwaIGERERERERERERERGZBd5Tg4iIiIiIiIiIiIiIzAKLGkREREREREREREREZBZY1CAiIiIiIiIiIiIiIrPAogYREREREREREREREZkFFjWIiIiIiIiIiIiIiMgssKhBRERERERERERERERmgUUNIiLSOSGEsbtAREREREQWhBmDiIgkLGoQEVGRZWRk4Nq1a6qfb9++jeHDhyMpKUnVFh8fj/r16+Ovv/4q8esIIZCeno709PQ8H09ISND6+ddff8WVK1dUPx87dgxRUVG5louJicGJEydw+vTpXP8dP34cT5480Xr+mjVrMHr06BKPg4iIiIiICsaMQURExcWiBhERFdnMmTMREBCA48ePAwDS0tKwfft2fPDBB6rnrFixAjdv3oSLi0uu5Rs2bAiZTFbof3K5HA4ODlizZk2udTx9+hR+fn5Yu3atqm3WrFm4cOECAGWoeOONN/DJJ5/kWvbq1auYNm0aRowYgT59+uDLL7/Exx9/jLZt2+Krr77CxYsX8eOPPyIgIAAAEB0djZs3b5ZuoxERERERUb6YMYiIqLhsjd0BIiIyH1999RVOnTqF119/HXfu3EHDhg0xa9YsLFy4ELGxsXBwcMBPP/2ETz/9FAMGDMi1vLu7O95991188cUXWu0rVqxA/fr10a5dO1VbZmYmypcvn2sdlSpVwrfffotRo0ZBoVBgxIgRsLGxgZ2dHdLS0vDaa6/Bx8cHK1asyLVs586dERwcjB9//BFHjhzB7t27ceLECXTs2BFHjhwBANy8eROOjo4AAHt7e9jb25diixERERERUUGYMYiIqLhY1CAioiKJi4tDamoqFi5ciEuXLiEpKQlJSUkYPHgwevfujfT0dCxYsAAKhQIfffSR6jTr8uXLw87ODgBgZ2cHNzc3+Pj4aK178+bNaNasGYYNG5bv66empuLOnTtwcHBAr169MGTIEMTFxSEiIgJZWVmIjo7G+fPnERMTg5UrVyIqKgrp6elwdXWFt7e31royMjLyDRJyuRwymQwAVP8nIiIiIiLdY8YgIqKSYFGDiIiK5J133sG2bduK9NxatWqp/n327Fn4+/urfs5rEq9QKLSWycv169fRvHlz2NnZwcnJCQCwa9cu1Sngn3/+uSrY9OzZE0IIpKWlYdy4cVi8eDGio6OxcOFCODg44MSJE3jy5AnmzJmDyMhIAMC3334LNzc3yOW8MiMRERERkSEwYxARUUmwqEFEREVib2+PN954I89r0OYlMjIS9erVy3W0kkwmQ3p6OoQQcHBwAACkpKSgYsWKBa6vSZMmSE1NVS0DABMnTsSOHTsQHx+PpUuX4v79+7h27Rp++umnXKeVp6Wl4fLly7C3t0dYWBicnZ1x8eJFxMTEAAAuXboEb29v1KxZEwqFokhjJCIiIiKikmPGICKikmCpmIiIisTZ2RkVKlSATCbD06dP8fz5c9Xp4Tn/y8zMhKurq+qmfRIhBORyOVauXAlHR0fVTfsePHiATz/9NNfN/Pbv369aVrqxHwBERUWhX79+2LZtG4KCglCmTBkoFAp0794dFy5cQL169bB+/Xqt/letWhW7du3C1q1bYWdnh0mTJmHDhg345ptvAABbtmzB4sWLkZqaitTUVP1vUCIiIiIiK8eMQUREJcGiBhERFcny5cuxZMkShIaGwsfHB97e3qhQoUKe/3300Ufw9vbGlStX0LhxY9U6UlNTYWtri8GDB+PmzZu4e/curl+/DgBYt24dwsPDER4ejt27dwNAruvU3rx5E19++SVq1aoFmUyGM2fOoF69ekhLS0NaWhqaN2+Os2fPYty4cRg+fDhee+01PH/+XGsda9aswcOHD9GrVy8AQFZWFgDlNXABYPTo0fjrr79Uj/FUcSIiIiIi/WDGICKikuCnKBERFUvz5s2RkJCAjIwMCCEghMCzZ88wbtw4yGQytG/fHiNGjMhz2cTERLi5ucHDwwO1a9eGr68vMjMzAQCdOnWCj48PfHx84OjoCACoUKGCatmsrCy8/fbb2Lt3L9avX4+dO3eiUqVKAIAGDRqojnyys7PD999/j+3bt0Mmk8HFxQWA8pq6v/76K9577z2MHz8e1atXB6A8ZRwA0tPTAQCVKlVCnTp18P3332PVqlUoV66crjchERERERFpYMYgIqLi4D01iIioWGxsbODq6qr6+cmTJ2jcuDFsbGzw119/YdiwYfkue+/evVzXoT1x4gSqVKmCKlWqqNqePXsGAFrPTUhIwIIFC+Dp6Qm5XI6IiAjVY1FRUbh27ZpWW5MmTTB79mzcuXMH9erVAwAcOnQIXbp0wU8//aR6XvPmzfHtt99i1qxZmDt3rqr98ePHKFu2LL788ssibhkiIiIiIioJZgwiIioOFjWIiKhIrl69CkAZODTFxsYiJiYGK1euRIsWLVSnegPKI5O8vb3h6emJhw8f4sWLF6ojnySBgYHo2bOnVtvTp0/h7OwMW1v1n6lt27bhww8/hJ2dnVYfsrKykJSUhFu3biEwMFDVLoRAeno6PDw88PDhQ8jlcqxevRqpqal48OABHBwcVOupUKECFi5ciIkTJ6qW/+KLL5CWlgZfX9+SbjIiIiIiIioAMwYREeON2aUAAAN9SURBVJUEixpERFQkw4cPx+3bt7VCgKaPP/5Y6zFpwv/bb79h1KhRCAkJAQDUrl1b9ZygoCAEBwdj/vz5Wut68uQJypYtq9X2zjvv4J133sn1ujNmzEBgYCCioqKwYcOGXOFFk7OzM3744Qd8//33ANThSQgBhUKBqlWrqp6blZUFBwcH3tCPiIiIiEhPmDGIiKgkZEIIYexOEBGR+UpKSoKrqysOHz6MLl265Pu8jz76CBs2bEB0dDRkMhnu3buHgIAAdO7cGRs2bNB67vDhw3H58mWEhoYW+Nr79+/Hq6++ikOHDuHBgwcYP348Vq5ciddeey3fZaQA4eDgAJlMBgDYsmULPvroIzx58kT1vKysLKSmpsLZ2bmwTUBERERERDrEjEFERAXhmRpERKR3SUlJWLt2Lfr37w+ZTIYTJ07gzTffhIODA3777TfV89auXYuwsDD8888/6NatW77ru337Nv73v/9h2bJl+OOPP9ChQwcAyhv4jRw5EmvXrsXkyZPRrl071TIZGRm4fPkyypQpA7lcrrW+R48eISsrS+u0dokUOjSP/iIiIiIiIuNixiAisl4sahARUYk8evQI165dw40bNwAg31PGAWDOnDmIi4vDqFGjcO/ePYwcORK2trY4cOAA3N3dVc+Lj4/H999/j1q1auGzzz7TWsfVq1exfPlynDp1CmfOnEFAQAD+/fdftGnTRvWcQYMGoVGjRpgwYQLat2+PWrVqYdSoUfjyyy8RGxuLdu3awcHBIVfgkGiuS5KWloaBAwdi3bp1xdk8RERERERUTMwYRERUFLz8FBERlcjTp09RvXp1ZGRkoE2bNti3bx9cXV3zfO6LFy+wfft2vPXWWwCA6OhopKWlaV1fVnpeYmIivLy8cq3j2bNn6NWrF9q2bYthw4YhICCgwP6dPHkSy5Ytw+DBg9GrV68SjpKIiIiIiAyFGYOIiIqCRQ0iIiqxyMhIeHp68pqwRERERESkE8wYRERUGBY1iIiIiIiIiIiIiIjILOR9wT8iIiIiIiIiIiIiIiITw6IGERERERERERERERGZBRY1iIiIiIiIiIiIiIjILLCoQUREREREREREREREZoFFDSIiIiIiIiIiIiIiMgssahARERERERERERERkVlgUYOIiIiIiIiIiIiIiMwCixpERERERERERERERGQW/h8bDHx4aZ4Q7AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# --- 全局设置：解决中文显示问题 ---\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# （2）整合学历类型：高中、中专合并到学历不限（原有代码）\n",
    "edu_mapping = {\n",
    "    '高中': '学历不限',\n",
    "    '中专': '学历不限'\n",
    "}\n",
    "job_copy['edu_merged'] = job_copy['edu'].replace(edu_mapping).fillna('学历不限')\n",
    "\n",
    "# 1. 查看合并前后的学历分布对比（原有打印代码）\n",
    "print(\"合并前的学历分布：\")\n",
    "edu_before = job_copy['edu'].value_counts(dropna=False)\n",
    "print(edu_before)\n",
    "\n",
    "print(\"\\n合并后的学历分布 (edu_merged)：\")\n",
    "edu_after = job_copy['edu_merged'].value_counts()\n",
    "print(edu_after)\n",
    "\n",
    "# 2. 查看具体合并效果（原有代码）\n",
    "print(\"\\n查看合并效果（前10行）：\")\n",
    "sample_view = job_copy[['edu', 'edu_merged']].head(10)\n",
    "print(sample_view)\n",
    "\n",
    "# 3. 详细统计（原有代码）\n",
    "original_counts = job_copy['edu'].value_counts()\n",
    "merged_counts = job_copy['edu_merged'].value_counts()\n",
    "\n",
    "print(\"\\n详细统计：\")\n",
    "print(f\"原本 '高中' 的数量: {original_counts.get('高中', 0)}\")\n",
    "print(f\"原本 '中专' 的数量: {original_counts.get('中专', 0)}\")\n",
    "print(f\"原本缺失 (NaN) 的数量: {job_copy['edu'].isna().sum()}\")\n",
    "print(f\"合并后 '学历不限' 的总数量: {merged_counts.get('学历不限', 0)}\")\n",
    "\n",
    "# --- 核心：学历合并前后分布对比可视化（修复tick_params参数错误） ---\n",
    "edu_before_cleaned = edu_before.rename(index={None: '未知'})\n",
    "len_before = len(edu_before_cleaned)\n",
    "len_after = len(edu_after)\n",
    "\n",
    "# 扩展样式列表，循环复用避免长度不匹配\n",
    "colors = ['#FFE6E6', '#E6F7F7', '#E6F3FF', '#F0F7E6', '#FFF9E6', '#FFE6F3', '#F3E6FF', '#E6E6FF']\n",
    "patterns = ['/', 'o', 'x', '\\\\', '+', '*', '-', '|']\n",
    "colors_before = [colors[i % len(colors)] for i in range(len_before)]\n",
    "patterns_before = [patterns[i % len(patterns)] for i in range(len_before)]\n",
    "colors_after = [colors[i % len(colors)] for i in range(len_after)]\n",
    "patterns_after = [patterns[i % len(patterns)] for i in range(len_after)]\n",
    "\n",
    "# 设置图表布局\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))\n",
    "\n",
    "# 绘制合并前的学历分布柱状图（修复刻度设置）\n",
    "bars1 = ax1.bar(\n",
    "    edu_before_cleaned.index,\n",
    "    edu_before_cleaned.values,\n",
    "    color=colors_before,\n",
    "    hatch=patterns_before,\n",
    "    edgecolor='gray',\n",
    "    linewidth=1\n",
    ")\n",
    "ax1.set_title('合并前学历分布', fontsize=16, pad=20)\n",
    "ax1.set_xlabel('学历类型', fontsize=12)\n",
    "ax1.set_ylabel('岗位数量', fontsize=12)\n",
    "# 修复：移除ha='right'，用labelrotation设置标签旋转\n",
    "ax1.tick_params(axis='x', rotation=45, labelsize=10)\n",
    "# 单独设置x轴标签的水平对齐（通过set_xticklabels实现）\n",
    "ax1.set_xticklabels(ax1.get_xticklabels(), ha='right')\n",
    "ax1.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# 合并前添加数值标签\n",
    "for bar in bars1:\n",
    "    height = bar.get_height()\n",
    "    ax1.text(\n",
    "        bar.get_x() + bar.get_width()/2,\n",
    "        height + 0.5,\n",
    "        f'{int(height)}',\n",
    "        ha='center', va='bottom',\n",
    "        fontsize=10, fontweight='bold'\n",
    "    )\n",
    "\n",
    "# 绘制合并后的学历分布柱状图（同样修复刻度设置）\n",
    "bars2 = ax2.bar(\n",
    "    edu_after.index,\n",
    "    edu_after.values,\n",
    "    color=colors_after,\n",
    "    hatch=patterns_after,\n",
    "    edgecolor='gray',\n",
    "    linewidth=1\n",
    ")\n",
    "ax2.set_title('合并后学历分布', fontsize=16, pad=20)\n",
    "ax2.set_xlabel('学历类型', fontsize=12)\n",
    "ax2.set_ylabel('岗位数量', fontsize=12)\n",
    "# 修复：移除ha='right'，用labelrotation设置标签旋转\n",
    "ax2.tick_params(axis='x', rotation=45, labelsize=10)\n",
    "# 单独设置x轴标签的水平对齐\n",
    "ax2.set_xticklabels(ax2.get_xticklabels(), ha='right')\n",
    "ax2.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# 合并后添加数值标签\n",
    "for bar in bars2:\n",
    "    height = bar.get_height()\n",
    "    ax2.text(\n",
    "        bar.get_x() + bar.get_width()/2,\n",
    "        height + 0.5,\n",
    "        f'{int(height)}',\n",
    "        ha='center', va='bottom',\n",
    "        fontsize=10, fontweight='bold'\n",
    "    )\n",
    "\n",
    "# 统一Y轴刻度范围\n",
    "max_count = max(edu_before_cleaned.max(), edu_after.max())\n",
    "ax1.set_ylim(0, max_count + 2)\n",
    "ax2.set_ylim(0, max_count + 2)\n",
    "\n",
    "# 调整布局并保存、显示图表\n",
    "plt.tight_layout()\n",
    "save_path = os.path.join(os.path.expanduser(\"~\"), \"学历合并前后分布对比图.png\")\n",
    "plt.savefig(save_path, dpi=600, bbox_inches='tight')\n",
    "print(f\"\\n对比图表已保存至: {save_path}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5c5705ee-a1d7-49dd-8628-dad9707bcd9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "不同学历平均薪资：\n",
      "edu_merged\n",
      "博士      32.8\n",
      "大专       9.9\n",
      "学历不限    11.5\n",
      "本科      17.6\n",
      "硕士      20.6\n",
      "Name: salary_avg, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# （3）统计不同学历岗位的平均薪资并保留1位小数\n",
    "edu_salary = job.groupby('edu_merged')['salary_avg'].mean().round(1)\n",
    "print(\"\\n不同学历平均薪资：\")\n",
    "print(edu_salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f360adb5-0877-415a-b7a3-3bf458f6acbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "不同学历平均薪资：\n",
      "博士      32.8\n",
      "硕士      20.6\n",
      "本科      17.6\n",
      "学历不限    11.5\n",
      "大专       9.9\n",
      "dtype: float64\n",
      "\n",
      "图表已保存至: C:\\Users\\86151\\不同学历平均薪资.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# --- 全局设置：解决中文显示问题 + 美化图表 ---\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams['axes.spines.top'] = False  # 隐藏上边框\n",
    "plt.rcParams['axes.spines.right'] = False  # 隐藏右边框\n",
    "\n",
    "# （3）统计不同学历岗位的平均薪资（你的原有代码）\n",
    "# 假设 job 是你的 DataFrame，且 'edu_merged' 和 'salary_avg' 列已存在\n",
    "# edu_salary = job.groupby('edu_merged')['salary_avg'].mean().round(1)\n",
    "# print(\"\\n不同学历平均薪资：\")\n",
    "# print(edu_salary)\n",
    "\n",
    "# !!! 在你的实际代码中，请删除下面两行，并取消上面两行的注释 !!!\n",
    "edu_salary_data = {'学历不限': 11.5, '大专': 9.9, '本科': 17.6, '硕士': 20.6, '博士': 32.8}\n",
    "edu_salary = pd.Series(edu_salary_data).sort_values(ascending=False) # 降序排列\n",
    "\n",
    "print(\"\\n不同学历平均薪资：\")\n",
    "print(edu_salary)\n",
    "\n",
    "# --- 核心：横向条形图可视化 ---\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# 定义一个专业、美观的渐变色板\n",
    "colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FECA57']\n",
    "\n",
    "# 绘制横向条形图\n",
    "# barh 的第一个参数是 y 轴标签（学历），第二个参数是 x 轴数值（薪资）\n",
    "bars = plt.barh(\n",
    "    edu_salary.index, \n",
    "    edu_salary.values, \n",
    "    color=colors[:len(edu_salary)], # 为每个柱子分配颜色\n",
    "    edgecolor='white', \n",
    "    linewidth=2\n",
    ")\n",
    "\n",
    "# 为每个柱子添加数值标签\n",
    "for bar, salary in zip(bars, edu_salary.values):\n",
    "    # bar.get_width() 获取柱子的宽度（即薪资值）\n",
    "    # bar.get_y() + bar.get_height()/2 确保文本垂直居中\n",
    "    plt.text(\n",
    "        bar.get_width() + 0.5,  # 文本在柱子右侧，留出一些间距\n",
    "        bar.get_y() + bar.get_height()/2,\n",
    "        f'{salary} K',          # 格式化薪资，加上单位\n",
    "        va='center',            # 垂直居中\n",
    "        ha='left',              # 水平左对齐\n",
    "        fontsize=11,\n",
    "        fontweight='bold'\n",
    "    )\n",
    "\n",
    "# 添加标题和坐标轴标签\n",
    "plt.title('不同学历数据分析岗位平均薪资对比', fontsize=16, pad=20, fontweight='bold')\n",
    "plt.xlabel('平均薪资（K）', fontsize=14)\n",
    "plt.ylabel('学历', fontsize=14)\n",
    "\n",
    "# 添加网格线（只在x轴方向添加，使读数更方便）\n",
    "plt.grid(axis='x', linestyle='--', alpha=0.3, color='gray')\n",
    "\n",
    "# 调整x轴范围，让最右侧的数值标签也能完整显示\n",
    "plt.xlim(0, edu_salary.values.max() * 1.15)\n",
    "\n",
    "# 调整布局，防止标签被截断\n",
    "plt.tight_layout()\n",
    "\n",
    "# 保存图表到指定目录（可选）\n",
    "save_path = os.path.join(os.path.expanduser(\"~\"), \"不同学历平均薪资.png\")\n",
    "plt.savefig(save_path, dpi=600, bbox_inches='tight')\n",
    "print(f\"\\n图表已保存至: {save_path}\")\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cecea7c4-8701-4fa9-922e-f2765ccbc724",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "不同学历岗位公司需求量：\n",
      "edu_merged\n",
      "博士        5\n",
      "大专      231\n",
      "学历不限     64\n",
      "本科      885\n",
      "硕士       91\n",
      "Name: company_name, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# （4）统计不同学历岗位的公司需求量\n",
    "edu_company_count = job_copy.groupby('edu_merged')['company_name'].nunique()\n",
    "print(\"\\n不同学历岗位公司需求量：\")\n",
    "print(edu_company_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6b5fe6b6-a267-4b04-8ad6-406fc58622f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "不同学历岗位公司需求量：\n",
      "edu_merged\n",
      "博士        5\n",
      "大专      231\n",
      "学历不限     64\n",
      "本科      885\n",
      "硕士       91\n",
      "Name: company_name, dtype: int64\n",
      "\n",
      "图表已保存至: C:\\Users\\86151\\不同学历公司需求量.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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9rLPO0kcffaQePXpo2bJl6tmzZ8gQtE6dOmnLli2V1i5JQ4YMqXQQXdnvg8vl0v333x+yh8JPP/2kbt26ac2aNfJ6vbr55ptD7rdmzRqVlJQoISEh5P9b2r37+aJFi3TFFVfo2GOP1c6dO4PH2/fv31/jx49XIBDQXXfdpbFjx+ruu++ussaRI0eqW7duIYPbPvzwQ73xxht66aWXgttKSko0YMAAPfroozTdALAfaLoBADEpNzdXV199td58801dc801eu655yrdTfn+++/Xrl279Pjjj+v888+vsvEubS6///57HXvsscHt4Sufe1KvXr1yp/i6++67NXHixOBxuIMHD65yIra0+1joOnXqlHttW7Zs0bp16zRy5MiQ7dnZ2ZIqPu1U2cd46qmnNGrUKI0dO1a33367hg0bpu3bt6tHjx5au3ZtubrKNohdunSp9BRd8fHxuvDCCzVr1izl5+crJSVFgUBA33zzjbp06VLlNPaKBqmVNWLECE2ZMkUPP/ywXnnlFUm7Pzwo+30+66yz9Oyzz+qiiy5S7dq1Q17vtm3b9O6771aak06dOmnlypXBwXBl1atXT8cee2zwdF1lFRUVhXzgUpVevXppx44dwdOqlbVr1y4VFxdXeIy+MUbFxcXq1KlThYcYNGvWTJ9//rlWr16t9u3b688//5QkXX755Vq2bJnuuOMOvfLKK+W+pxVJSkrShx9+qPnz5we3lZSUqLi4WIMHDy53+73ZFR0AUF7VB1kBABAFb775po466ii9/fbbmjRpkl544YU9Hhc8efJkXX755fr8889DVmDDVfU4e3qOgoICFRQUqKSkJHgu6rJfK1euVHp6un7++edg81J6XXFxsXJzc8vtwvzbb78pIyOj3HPNmTNHxhh17do1ZPu2bdskqcJTTpVt8kaOHKkff/xRZ599tm6//Xa1atVKJ510ktavX68FCxZo0KBBVb7W8PNCl3X++eerqKgoeH7rNWvWKDc3t8Lp4GWVDlIr/UpOTg65vnHjxurRo4dmz54dfJ0FBQXBY55LXXfddSEfOuTk5Ojhhx9Wv379QnbT/u2333TJJZcoNzc3uK2ihluqXmO5pw9QpN2n2crPz1d2draysrJCvv75z3/q8ssvL7c9KytL2dnZysvLq7DhLjVr1iy1b99eLVq0CH4oERcXpyeeeELGGP399997rE/avffAtddeq7Vr1wa/7rrrLqWlpYVsW7VqlaS9P8c9ACAUK90AgJizdetWGWO0YMGCSldcwzmOo2nTpqmoqCg4EKwipad1+uOPP0KOFd61a1elU7JLDR06tFqnHEtPT6/0ug0bNqhJkyaSpEAgoO+//z7ktGLS7uOYJ0+erPbt21e4y7mkkFXeUuErq0cddZRmz56tWrVqBXcbbtu27X6vXJ555plyuVz66KOP1LNnz+BU9vAPCPbFFVdcIUnBDyfy8/Or/H5Ku0+tVVBQoAcffDBk+6ZNm/TWW2+pTp06e9wtvPS5Ss+pXlZxcXF1y5e0e8p7+OT3Uj/++GOlx/fffPPNwd3Gy8rLy9OIESP06quvaty4cRozZkzIKnW3bt1044036s4775TL5dLtt9++xxpffPFFvfjii8HLfr9fPp+v3EwDj8ez168fABCKphsAEHNKB5ZVdWx2ReLj4/XGG29UeZvScw6X3c25VIcOHaq878CBA9W1a1fFx8dXuBL89NNPa/ny5SHNTCmfz6fCwsKQFervv/9eu3btCk7fLjVu3Dj9/vvveuSRR8o9zsaNGyVVvNIdviL59ddf64477tCCBQt01VVX6eSTT9btt9+uDh066MYbbyw3bKu66tSpo+OOO04//PCDJOmzzz5Tw4YNdeSRR+7T45XVu3dv9enTJ3g5Nze3wg8YSn3zzTd6+umnNXbs2HLD5U4//XTdddddGj9+vC655BKdddZZVT734sWLq3wN1W0+k5KSdNVVVwUHl02cOFH/+c9/gt+vOXPm6JZbbtEPP/wQ/H9s06ZNhXl/6623dNNNN6m4uFiffPKJunTpUuFzTp48WRs2bNAdd9yhH3/8UY8//ni5wx+k3buR5+bmlturY8qUKRo3blxwonqp0t3eAQD7jqYbABCT9rbhrq5evXrt8+6ye1p1f//997VmzRpdfPHF1Xq80indZ5xxRnDbs88+qwcffFD9+vWr8IOB0hXUBg0alLuu9JRUa9as0bXXXqtFixapVatW+vDDD3XOOedIknr27KmrrrpKTz/9tP71r3+pW7duldZX9vvk8/nk9/uDp0d799131ahRIwUCAc2bN0/nnntuyP2KioqCxzWXPs6mTZtCVpILCgrKHScdvlq/ZcuWSle6N23apH79+qlNmza67bbblJWVpS1btmjTpk36+++/tXHjxuCHFIMHD9aqVavK7dJeqri4WN27d6/ymO6ioqJKv1dleTwepaSkBPdoSE1NlcvlCl4ubbQbNmwYPLTAcZyQD3L8fr969Oihjz76SB06dNCbb74Zcp7ucHFxcXrzzTd1yy236IknntBHH32kq6++WqNGjQrZFf/uu+/WQw89pPj4+JDGu3Slu+zeH6UN9+GHHx48pzgAYO/RdAMADiqljeWeTu9Ual8b7NJTee2NkpKSat92586deuGFF1S7dm11795d27dv1+23366pU6eqbdu2euGFF4K3LS4uVv/+/bVjxw7Nnz9fmZmZFZ66qvS1tmnTRv/85z/Vp08fDR06NGS4WaNGjfTxxx/rl19+UZs2bfTjjz9Kqvhc4KWvxxijxYsXVzmZfObMmZo5c2bItk2bNumwww4LPs6dd96pO++8M+Q2la3cSrvPW52fn19uF3tp9zm5O3furE2bNik5OVmpqanBQwek3avNLVq0UIsWLdS3b1/NmTNHDzzwgB544IEKnys7O7vS4709Ho/WrVun1NTU4LbSY84PhF27dpXLm9vt1q233qpGjRrpmWeeCR768Oeff2rVqlV69913JSnkQwS3263HH39cp59+uoYNG6bk5ORy8wKGDh2qESNGKCMjI+QY9YpWukub7ry8vAP2WgHARjTdAICDSmljVd0Gd9euXXv1+K+++qrmzJmjJUuWSFK5IV7hfv75Z7344ovyer364IMPKpwqXpEJEyYoOztbd9xxhxISEvTGG2/oP//5j4466ih98sknIc+bkJCgxMREzZ8/X0cccUS5Y5dLlX5PXC6Xpk6dWulzO46jVatWadGiRZo9e7akindXL20EfT6f/vnPf2ratGlKSEjY48A5Y4wKCwuDpyorKSmRx+PRiy++GDxmW9q9+lz6IUqpjz/+WOPGjVN2drY2bNigYcOGqXv37uWeo2XLlqpXr57i4+N14oknqm3btmrVqpVatmypli1b6rDDDgv5IOH000/Xo48+qmuvvbbC5nrRokWVroI7jqPmzZurpKREl19+ubZu3apvv/1WkoKr12WFr4inp6erUaNGwcspKSlq3Lix3G63XnjhBd14443y+Xzlnr9r167ljpOPj49Xr1695Pf71blz53KHJkhS37591b1795BTxRljtHnzZiUnJysxMbFcjaW7kFf0fklISFBWVpYSEhJCHhMAUE0GAICDyD333GMkmbVr11br9uecc47Zm193ixYtMm6327Rs2dKMGTNmj7cvKSkxjRs3NsnJyeb44483c+bMqdbzBAIB88Ybb5icnJzgth9//NHk5uZWePtt27ZVel3p40ky99xzT7Wev3fv3kaSkWTat29vdu7cWe42f/zxh5Fkxo4dW63HPBCKi4vN008/bRYsWGAKCgqqvG3Z792efPXVV2b69OkmEAjsV3033XSTqVWrljn11FPN448/bvx+f7nbtGvXztxwww3VerwtW7YYl8tlTjvtNPP7779X6z6LFy8269ev36u68/LyjNvtNikpKaZevXp79VW7dm3j8XjMqFGj9uo5AQC7OcZwHggAAEoFAgEFAoFqnR6qVOnxyweT7777Tn///beOP/74Ko8VRqji4uIqz0W+L3Jzc1lBBoBDGE03AAAAAAARUvVBWQAAAAAAYJ/RdAMAAAAAECE03QAAAAAARAhNNwAAAAAAEULTDQAAAABAhNB0AwAAAAAQITTdAAAAAABECE03AAAAAAARQtMNAAAAAECE0HQDAAAAABAhNN0AAAAAAEQITTcAAAAAABFC0w0AAAAAQITQdAMAAAAAECE03QAAAAAARAhNNwAAAAAAERIX7QJsFwgEtHHjRqWmpspxnGiXAwAAAACoBmOM8vLy1KhRI7lcla9n03RH2caNG9W0adNolwEAAAAA2AcbNmxQkyZNKr2epjvKUlNTJUl//vmnateuHd1igBoSCAS0bds21a9fv8pPBYFDCbmHjcg9bETu7ZGbm6umTZsGe7rK0HRHWeku5enp6UpLS4tyNUDNMMYoEAgoPT2dwypgDXIPG5F72Ijc22dP/8803TGCNyRs4jiO6tatG+0ygBpF7mEjcg8bkXuEY3+HGGGMiXYJQI0pHTpB7mETcg8bkXvYiNwjHE13jOBNCZsYY5Sfn0/uYRVyDxuRe9iI3CMcTTcAAAAAABFC0w0AAAAAQITQdMcIBqnBJo7jKCkpidzDKuQeNiL3sBG5Rziml8cI3pSwieM4Sk9Pj3YZQI0i97ARuYeNyD3CsdIdIxi0AJsYY7Rz505yD6uQe9iI3MNG5B7haLpjBG9K2MQYo8LCQnIPq5B72Ijcw0bkHuFougEAAAAAiBCabgAAAAAAIoSmO0YwSA02cRxHKSkp5B5WIfewEbmHjcg9wjG9PEbwpoRNHMdRampqtMsAahS5h43IPWxE7hGOle4YwaAF2MQYo+3bt5N7WIXcw0bkHjYi9whH0x0jeFPCJsYYFRcXk3tYhdzDRuQeNiL3CEfTDQAAAABAhNB0AwAAAAAQITTdMYJBarCJ4zhKS0sj97AKuYeNyD1sRO4RjunlMYI3JWziOI6Sk5OjXQZQo8g9bETuYSNyj3A03TGisLBQHo8n2mUcctxut+LiiHmsCQQC2r59u+rWrSuXix1uYAdyDxuRe9iI3CMc3UiMmDJlihITE6NdxiGnVlKKbrplBI13DPL5fNEuAahx5B42IvewEblHWXQiMeL6RlepQUpGtMs4pHgDxZr89wvy+/003QAAAACigk4kRnhcCfK42L0cAAAAAA4lHGQQIxijBps4jqM6deowQBBWIfewEbmHjcg9wrHSHSMc2m5YxHEcBgfCOuQeNiL3sBG5RzhWumNEQCbaJQA1JhAIaMuWLQoEAtEuBagx5B42IvewEblHOJpuAFFhDB80wT7kHjYi97ARuUdZNN0AAAAAAEQITTcAAAAAABFC0x0jGKMGmziOo3r16jHVE1Yh97ARuYeNyD3C0XTHCKaXwyaO48jtdvPLCFYh97ARuYeNyD3C0XTHCKaXwyaBQEBbt25lqiesQu5hI3IPG5F7hKPpBgAAAAAgQmi6AQAAAACIEJpuAAAAAAAihKY7RrgYpAaLuFwuZWZmyuXiRxDsQe5hI3IPG5F7hCMJMcIwSA0WMcbI7/fLGHIPe5B72Ijcw0bkHuFoumMEb0nYxBij7OxsfhnBKuQeNiL3sBG5RziabgAAAAAAIoSmGwAAAACACKHpBhAVjsPwQNiH3MNG5B42IvcoKy7aBWA3ppfDJi6XSw0aNIh2GUCNIvewEbmHjcg9wrHSHSOYXg6bGGPk9XoZMAKrkHvYiNzDRuQe4Wi6YwRvSdjEGKMdO3bwywhWIfewEbmHjcg9wtF0AwAAAAAQITTdAAAAAABECE03gKiIi2OOI+xD7mEjcg8bkXuURRpiBNPLYROXy6WMjIxolwHUKHIPG5F72IjcIxwr3TGC6eWwiTFGBQUFDBiBVcg9bETuYSNyj3A03TGCtyRsYoxRbm4uv4xgFXIPG5F72IjcI5yVTXdxcbECgUDI5ZKSkpBt0v8/xx4AAAAAAPvCyqa7d+/eatq0qVwul5o0aaKmTZvqlltuUbNmzXT44YcrPj5eDRs2VNOmTXX44YdX+jitW7fWM888U4OVY198+umn6tixo9LS0nTUUUdp9uzZIdevXbtW9evX1x9//BGdAgEAAAAcsqwcpPbBBx9o69atatWqlf7880+53W5J0pNPPiljjFJTU/X999/rsMMOq/JxkpOTlZSUdEBqYoxaZGzatEl9+vTRY489pjfffFOffPKJrrzySgUCAV122WVatWqVevbsqaysrGiXahXHcZSQkCDHIfmwB7mHjcg9bETuEc7KpluSPvvsM5166qnBhjs3N1cvv/yy8vPz5fP5gquhp556qtq3b1/hYziOc8DeTA5td0QsX75cp512mm688UZJUv/+/fXuu+/qtdde0wUXXKBu3brptttu04gRI6JbqGUcx1HdunWjXQZQo8g9bETuYSNyj3BW7l4uSdOnT1ffvn2Dl3ft2qX77rtPeXl5GjFihDZv3qx33nlHCxYskN/vl8/nq9bjBgKBfToOnOnlkZGfn1/uEIH4+Hi53W7Fx8dr2bJluuCCC6JUnb2MMcrLy2PACKxC7mEjcg8bkXuEs7LpXr58ud5//32NHDlSGRkZ6tevn+Li4lS/fn09+OCD6tSpk0aNGqVzzjlHcXFx+vDDDxUfHx9c2S79WrFihQYOHBiyze12V3kceGV4S0ZGo0aNNG/ePOXk5EiS1q9fr//+97/q0aOHEhIS1KxZs+gWaCljjPLz8/llBKuQe9iI3MNG5B7hrNu9vLi4WFdffbXi4+O1cOFC5eTk6Mknn5TL9f8/f7jxxhv1wgsvBC9369ZN27dvV1xcXMju5KeeeqpuuOEGXX755cFtFU1BL8vr9YashOfm5h6ol4YKnHTSSapVq5batWunE044QZ9//rnS0tJ02WWXRbs0AAAAABawrulesWKFEhISdPrppwe3OY4T0iiXlJSoWbNm+uabbyRJCQkJSkhIKPdYLpdLHo9HtWrVqvbzT5w4UePHj9+PV4C9ER8fr1mzZik7O1vffPON3n77bT355JNKTEyMdmkAAAAALGDd7uUnnXSSPvnkk5CVbUnBpruwsFCbN29WkyZNJOmA7xYyevRo7dy5M/i1YcMGSUwvj7RGjRppzpw56tSpE6vcMcBxHCUlJTHVE1Yh97ARuYeNyD3CWbfSLUlpaWnltpXu8v3111/riCOOUHJysiRVe4BadXk8Hnk8nnLbmV4eWd98841ef/11ffvtt9EuBdr9yyg9PT3aZQA1itzDRuQeNiL3CGfdSncpv9+viy++WAMHDlRJSYkaNGigl19+WWPGjNHAgQN1zTXXqKioqMZWRZleHjnGGA0bNkw33XSTjjrqqGiXA+3+P9m5cycDRmAVcg8bkXvYiNwjnJUr3ZLUoEEDPfvss0pOTtaXX36prKwsjRs3TnFxcRo+fLj++9//qn///srJydHkyZNVWFiobdu2hZzo3ufzaefOndq8ebOk3UPaXC5XcNf0vcFbMnIcx9GyZcsqvb558+b8UKxhxhgVFhYqNTWVXa9gDXIPG5F72IjcI5y1Tffs2bOD/27QoIFOOeUUtWnTRi+99JLi4uLUq1cvLVq0SJMmTVJxcbGWLVum7t27KyEhIeR48DFjxmjMmDGSdg9g69Chg7744osafz0AAAAAgNhjbdNdlsfj0aJFi5SSkhKyvV27dpo5c6Yk6ayzzjrgx3cDAAAAAA5t1h7THS684a5p7HgCmziOo5SUFHa5glXIPWxE7mEjco9wrHTHCKaXwyaO4yg1NTXaZQA1itzDRuQeNiL3CMdKd4xgejlsYozR9u3bGWAHq5B72Ijcw0bkHuFoumMEb0nYxBij4uJifhnBKuQeNiL3sBG5RziabgAAAAAAIoSmGwAAAACACKHpjhGMUYNNHMdRWloaUz1hFXIPG5F72IjcIxzTy2ME08thE8dxlJycHO0ygBpF7mEjcg8bkXuEY6U7RgQYpQaLBAIBZWVlKRAIRLsUoMaQe9iI3MNG5B7haLoBRIXP54t2CUCNI/ewEbmHjcg9yqLpBgAAAAAgQmi6AQAAAACIEJruGMEYNdjEcRzVqVOHqZ6wCrmHjcg9bETuEY7p5TGC6eWwieM48ng80S4DqFHkHjYi97ARuUc4VrpjBNPLYZNAIKAtW7Yw1RNWIfewEbmHjcg9wtF0A4gKY/igCfYh97ARuYeNyD3KoukGAAAAACBCaLoBAAAAAIgQmu4YwRg12MRxHNWrV4+pnrAKuYeNyD1sRO4RjqY7RjC9HDZxHEdut5tfRrAKuYeNyD1sRO4RjqY7RjC9HDYJBALaunUrUz1hFXIPG5F72IjcIxxNNwAAAAAAEULTDQAAAABAhNB0AwAAAAAQITTdMcLFIDVYxOVyKTMzUy4XP4JgD3IPG5F72IjcIxxJiBGGQWqwiDFGfr9fxpB72IPcw0bkHjYi9whH0x0jeEvCJsYYZWdn88sIViH3sBG5h43IPcLRdAMAAAAAECE03QAAAAAAREhctAvAbt5AsbwBb7TLOKR4A8XRLgFVcByGB8I+5B42IvewEblHWTTdMeLZja8oMTEx2mUccmolpcjtdke7DIRxuVxq0KBBtMsAahS5h43IPWxE7hHOMRzhH1W5ublKT0/Xli1blJ6eHu1yDjlut1txcXy2FGuMMSouLlZCQgKfBMMa5B42IvewEbm3R2kvt3PnTqWlpVV6O7qRGJGQkCCPxxPtMoAaYYzRjh07lJmZyS8jWIPcw0bkHjYi9wjHIDUAAAAAACKEphsAAAAAgAih6QYQFRxrDxuRe9iI3MNG5B5lkYYY4XLx+Qfs4XK5lJGREe0ygBpF7mEjcg8bkXuEo9OLEQyRh02MMSooKCD3sAq5h43IPWxE7hGOpjtG8KaETYwxys3NJfewCrmHjcg9bETuEY6mGwAAAACACKHpBgAAAAAgQmi6Y4TjONEuAagxjuMoISGB3MMq5B42IvewEblHOKaXxwjelLCJ4ziqW7dutMsAahS5h43IPWxE7hGOle4YwaAF2MQYo7y8PHIPq5B72Ijcw0bkHuFoumMEb0rYxBij/Px8cg+rkHvYiNzDRuQe4Wi6AQAAAACIEJpuAAAAAAAihKY7RjBIDTZxHEdJSUnkHlYh97ARuYeNyD3CMb08RhQXF8vr9Ua7DKDGJCYmqri4ONplADWK3MNG5B42Ivf7z+12Ky7u0GhXD41XcQh47LHHlJiYGO0ygBqTmJiooqKiaJcB1ChyDxuRe9iI3O+/WsmJuunmWw+JxvvgfwWHiOs6/q4Gtd3RLgOoEQHjKKu4oTISNsnlMNkTdiD3sBG5h43I/f7z+lyasrC1/H4/TTcOHE+ckSc+EO0ygBoRMI7iAwF54gP8MoI1yD1sRO5hI3KPcAxSAwAAAAAgQmi6Y4QjPgWDPRwZ1YrLI/ewCrmHjcg9bETuEY7dy2MEZxSATRxHqhWXF+0ygBpF7mEjcg8bkXuEY6U7RhhD1w17GONoe3E9cg+rkHvYiNzDRuQe4Wi6YwQ7n8AmRlJxwEPuYRVyDxuRe9iI3CMcTTcAAAAAABFC0w0AAAAAQITQdMcIphvCJo6M0uJyyD2sQu5hI3IPG5F7hGN6eYxgejls4jhSclxBtMsAahS5h43IPWxE7hGOle4YEWC6ISwSMI6yvJnkHlYh97ARuYeNyD3C0XQDiAqfYUcb2Ifcw0bkHjYi9yiLphsAAAAAgAih6QYAAAAAIEJoumME0w1hE0dGdeKzyT2sQu5hI3IPG5F7hONggxjB9HLYxHEkj9sb7TKAGkXuYSNyDxuRe4RjpTtGMN0QNgkYR1uKGpJ7WIXcw0bkHjYi9whH0w0gKoz4RQT7kHvYiNzDRuQeZdF0AwAAAAAQITTdAAAAAABECE13jGC6IWziyCgjYSu5h1XIPWxE7mEjco9wTC+PEUwvh00cR3LJT+5hFXIPG5F72IjcIxwr3TGC6YawScA42uplqifsQu5hI3IPG5F7hKPpBgAAAAAgQmi6AQAAAACIEJpuAAAAAAAihKY7RrgcphvCHi7HKNOzidzDKuQeNiL3sBG5Rzia7hhheE/CIsZIAeMm97AKuYeNyD1sRO4RjqY7Rhgx3RD2MHKUVZxJ7mEVcg8bkXvYiNwjHE33/zNo0CDdeeedVd5m165dWrhwocaMGaPBgweXu75169aaN29epEoEAAAAAKu89dZbatOmjRITE9W8eXM98cQTkiSfz6frrrtO9evXV0pKik4//XQtX748eL8ff/xRjuOEfF144YVReQ1xUXnWKPP7/XK73SHbkpKSlJCQUOFtCwoKdNppp2nTpk3KycnRHXfcoX//+98KBAIaPny4rrrqKp144onyeDyKi7PyWwoAAAAAB9Qff/yhQYMGafbs2WrXrp2WLl2q/v3765///KeWL1+uVatWadmyZYqLi9NDDz2k888/Xxs2bJDL5dLSpUt11lln6Y033gg+Xnx8fFReh5Ud4tlnn61ly5bJ4/FIkoqLi1VUVCRJeuSRR4KNc3Fxsbp27aq5c+dq3rx5yszM1GGHHaZBgwapefPmkqSOHTuqZ8+e+vrrr6PyWoCDlSMOdIJ9yD1sRO5hI3J/YHz77bdq1aqVunfvLknq06ePjjzySK1Zs0aLFi3Sv//9b7Vq1UqSNHToUD333HPauXOn6tSpo6VLl+rMM89U7dq1o/gKdrNy9/IFCxaosLBQOTk5ysnJ0WmnnaY6deooIyND1113XXB7QUGB5s6dK5/Pp9TUVJky0xB8Pp8CgYAuvfRS9evXTy+//PJ+1cR0Q9jE5Rg1SGSqJ+xC7mEjcg8bkfsDp23btlq5cqXef/99FRQU6K233tLq1avVvXt3HXPMMZo2bZr+/PNP7dixQ5MmTdKpp56qOnXqSJKWLFmiWbNmqVatWqpfv75uuOEGFRYWRuV1WLnSXdbvv/+u5cuX68ILL1RcXJzefPNNPfTQQyG7ny9YsEAXXnihEhMTtWPHDh1//PEKBAL69NNPtXbtWj388MNyu916/fXX97kOphvCJsZIxQGPElxeOcwYgSXIPWxE7mEjcn/gHHnkkRo9erR69uwZ3PbMM8+obdu2Gjt2rN57773gHsiHHXaYlixZIknavn27cnNzdc8996h379767bffdPnllyslJUWTJk2q8ddh5Up3WQ899JAGDx6spKQkNWzYUEcccYRee+21kNt069ZNBQUFeueddyRJH3zwgRYvXqyGDRvqrrvu0ujRo6v9fF6vV7m5uSFfEtPLYRcjRztK6pF7WIXcw0bkHjYi9wfOihUr9Mgjj+itt95SQUGBvvjiC02cOFFz5szR2LFjVatWLf3yyy/Kzc3V8OHDdeaZZ2rHjh2qW7eutm7dqhtuuEGNGjVSx44dNW7cOE2fPj0qr8PqpvvHH3/UO++8o9tuuy247f7779edd96pnJyccrcvnZS3fPlyde/eXdnZ2frss8/07rvvavHixcHbeb3ekF3Ry5o4caLS09ODX02bNj2wLwoAAAAADgGzZs1S165d1bt3byUlJaljx4664YYb9Mwzz+jll1/WnXfeqdatWys1NVWjR49WcnKy5s6dW+FjNWrUSJs3bw7O8qpJ1jbdu3bt0uWXX64HHngguN+/JJ1wwgk6//zzdemll6q4uDi4fcmSJfrtt9+UmZmpc889Vw8++KC2b98uSXr00Uf11VdfafPmzRo4cKCOOOIIbdq0qcLnHT16tHbu3Bn82rBhQ2RfKAAAAAAchHw+nzZv3hyybfPmzfL7/eWuKykp0fbt2+X3+/Xhhx+qd+/eIfdbvHixMjMzlZiYWCO1l2XlMd1er1eXXXaZ2rVrp8suu0yBQCB4XSAQ0AMPPKALL7xQ559/vl5//XX5/X4NGDBATz31lAYOHChp90H9Xbp0UdOmTdW+fXsdffTRSkpK0pNPPqkePXpU+twejyc4NR2wWZzji3YJQI0j97ARuYeNyP2B0b59ez3zzDO64447dPzxx+vHH3/U008/rUceeURJSUl64IEHJEkpKSmaMWOGCgoK1L17d8XFxWn+/Pl66KGHdM4552jJkiV69NFHddddd0XldVjXdG/ZskUXXHCB0tLS9PLLL6tevXpKSkpSYWGhHMfR448/rsLCQmVlZenSSy9Vly5d9NBDD+mMM87QueeeG3yc448/XuvWrVPdunWD21566SXVqlVrn+piuiFs4nKMMjxbo10GUKPIPWxE7mEjcn/gXHrppcrJydEzzzyjKVOmKD09XTfddJNuvPFGXXLJJRo1apTGjRunrKwstWnTRnPmzFHjxo0lSe+9956GDx+usWPH6vDDD9cTTzyhwYMHR+V1WNd016tXT1dffbUGDBigxMTE4Nj4a6+9VpmZmbrvvvuCt507d642bNig5s2bq1u3bpIkY4yMMXIcJ6ThliS/3x+yar43mF4OmxgjFfqTleQuYKonrEHuYSNyDxuR+wNr+PDhGj58eLnthx12WJWD0Tp37qwVK1ZEsrRqs+6Y7ri4OA0ZMqTcvvxerzfkGG5JcrvdwRH0pYqLiys9+L6wsLDcY1QX0w1hEyNHub7a5B5WIfewEbmHjcg9wlm30l2ZV155pVq327FjR6XX/fHHHwemGAAAAADAIcG6lW4AAAAAAGoKTXeMYOcT2MSRlODykntYhdzDRuQeNiL3CMfu5THCYXo5LOI4RnUTsqNdBlCjyD1sRO5hI3KPcKx0xwiml8Mmxki7fKnkHlYh97ARuYeNyD3C0XTHCKYbwiZGzu5fRuQeFiH3sBG5h43IPcLRdAMAAAAAECE03QAAAAAARAhNd4xg5xPYxJGU5C4g97AKuYeNyD1sRO4RjunlMYLp5bCJ4xilx+dEuwygRpF72Ijcw0bkHuFY6Y4RxvBZGOxhjKOdJbXJPaxC7mEjcg8bkXuEo+mOEaxzwyZGUqE/mdzDKuQeNiL3sBG5RziabgAAAAAAIoSmGwAAAACACKHpjhEOO6DAIo6MasXlkXtYhdzDRuQeNiL3CMf08hjhMGcBFnEcqVZcXrTLAGoUuYeNyD1sRO4RjpXuGMF0Q9jEGEfbi+uRe1iF3MNG5B42IvcIR9MdI9j5BDYxkooDHnIPq5B72Ijcw0bkHuFougEAAAAAiBCabgAAAAAAIoSmO0Yw3RA2cWSUFpdD7mEVcg8bkXvYiNwjHNPLYwTTy2ETx5GS4wqiXQZQo8g9bETuYSNyj3CsdMeIANMNYZGAcZTlzST3sAq5h43IPWxE7hGOphtAVPgMO9rAPuQeNiL3sBG5R1k03QAAAAAARAhNNwAAAAAAEULTHSOYbgibODKqE59N7mEVcg8bkXvYiNwjHAcbxAiml8MmjiN53N5olwHUKHIPG5F72IjcIxwr3TGC6YawScA42lLUkNzDKuQeNiL3sBG5RziabgBRYcQvItiH3MNG5B42Ivcoi93LY4TX58hbwmcgsEPAOCrxueR1ueRyON4JdiD3sBG5h43I/f7z+g6tvoimO0Y8t+gfSkxMjHYZQI1JS0tTbm5qtMsAahS5h43IPWxE7vdfreREud3uaJdxQDjGGD5+iaLc3Fylp6dry5YtSk9Pj3Y5QI0wxsjv98vtdsthiiAsQe5hI3IPG5H7A8PtdisuLrbXiEt7uZ07dyotLa3S28X2q7BIYmKiPB5PtMsAakwgEJDLdWjtOgTsCbmHjcg9bETuURZJiBGBQCDaJQA1JhAIaOvWreQeViH3sBG5h43IPcLRdAMAAAAAECE03QAAAAAARAhNNwAAAAAAEULTHSMYtACbuFwuZWZmkntYhdzDRuQeNiL3CEcSYgRnboNNSk+lQe5hE3IPG5F72IjcIxxNd4zgTQmbGGOUnZ1N7mEVcg8bkXvYiNwjHE03AAAAAAARQtMNAAAAAECE0HQDiArHcaJdAlDjyD1sRO5hI3KPsuKiXQB2Y7ohbOJyudSgQYNolwHUKHIPG5F72IjcIxydXoxg0AJsYoyR1+sl97AKuYeNyD1sRO4RjqY7RvCmhE2MMdqxYwe5h1XIPWxE7mEjco9wNN0AAAAAAEQITTcAAAAAABHCILUY4fV65fV6o11GjXG73YqLI3424/8fNiL3sBG5h43IPcoiDTFiypQpSkxMjHYZNSYpJVm3jLiZH0iWcrlcysjIiHYZQI0i97ARuYeNyD3C0fHEiLpnHqXk2qnRLqNGGJ9f2Z/+IL/fT9NtKWOMCgsLlZSUxHksYQ1yDxuRe9iI3CMcHU+McOJccsW7o11GjQhEuwBEnTFGubm5SkxM5JcRrEHuYSNyDxuRe4RjkBoAAAAAABFC0w0AAAAAQITQdAOocY7jKCEhgV2uYBVyDxuRe9iI3CMcx3THDN6UsIfjOKpbt260ywBqFLmHjcg9bETuES6iK91r165V//79I/kUhxAT7QKAGmOMUV5enowh97AHuYeNyD1sRO4Rrtor3QMHDtSvv/6qhISEcte5XC7VqVNHd911l9q1axfcnp+fr7fffvvAVArgkGGMUX5+vlJSUtj1CtYg97ARuYeNyD3CVbvp7tKli44++ugKj0/w+/165ZVXNGLECE2bNk1t27ZVdna2EhMT5fF4DnjRAAAAAAAcDKrddF9xxRVVXr9r1y5t2rRJHo9HRUVFSkxM3O/iAAAAAAA4mO3VILWTTz5ZSUlJwZXuQCCgoqIizZgxQ3feeackacuWLYqLi2NXCgCVchwn5GcJYANyDxuRe9iI3CPcXjXdF198sTwej1yu3fPX/H6/ioqKlJ6eHpHi7MKbEvZwHIefG7AOuYeNyD1sRO4RrtpN95YtW3TMMceoW7dukqQnn3xSv//+u6ZMmRKp2izDdEPYwxij3NxcpaWl8SkwrEHuYSNyDxuRe4Sr9inDfv31Vz344IPByzt37tTGjRsl7V7xLiws1B133KGjjjpKfr9fjRo1UqdOnQ58xQAOesYYFRYWcioNWIXcw0bkHjYi9whX7aY7Pj5en332mdxut9xut+655x69+eabcrvdSkhIUK1atZSWlqb7779fLpdLTz75pMaMGRPJ2gEAAAAAiGl7dUz3iSeeqMmTJ8sYo1mzZunXX3/VuHHj5Pf75fV6dfzxx8vn82nYsGG66KKL9PPPP2vcuHERKh0AAAAAgNi2V0137dq1ddppp0mSlixZopycnODlUlu2bDlw1QE4JDmOo5SUFI5zglXIPWxE7mEjco9we9V0f/fdd+rZs6cSEhK0du1aFRcXa+rUqTrnnHPUpEkTSbuP7/b7/REp9tDGmxL2cBxHqamp0S4DqFHkHjYi97ARuUe4ah/TLUl169ZV586ddeKJJ6pBgwbatWuXJkyYoFatWunmm2+Wz+eT1+uVMUbFxcWRqvkQxaAF2MMYo+3btzNgBFYh97ARuYeNyD3CVXul2+fzKTMzUyNHjpQklZSUKC0tTW+88YZeffVVXXfddfrll180ffr04AC1QCCgQCAQmcoBHLRKP5gzxrDrFaxB7mEjcg8bkXuEq3bTfcQRR2jChAnBy16vN7iafeWVV6qgoEALFixQUlKS7rvvPklSYWGhioqKDnDJAAAAAAAcHKrddNerVy/kvNs333yzvF5v8PJ1112n6667LuTTnGOOOUbr168/QKUCAAAAAHBw2atBamVlZGSEXK5o14m4uDhlZmbu61MAOEQ5jqO0tDR2uYJVyD1sRO5hI3KPcHs1SK3Upk2bNGLEiODlnTt3yu/36+eff1bTpk1DbjthwgR9/PHH+1WkHXhTwh6O4yg5OZlfRrAKuYeNyD1sRO4Rbp+a7ry8PL3wwgvBy4MGDdK///1vOY6j7OzskNu+8sorev311/evSisw3RD2CAQCysrKYtAirELuYSNyDxuRe4SrdtO9bds2XXbZZSoqKlJCQoI8Ho8kafbs2Xr77bc1YMCAkO2StGzZMq1du1bXXHPNga8cwEHN5/NFuwSgxpF72Ijcw0bkHmVVu+n+6aeftGDBAnXp0kVbt26V4zjasWOHhg8frjvvvFM9e/aU4zghu1E8+uijOvfcc3XyySdHpHgAAAAAAGJZtZvujh07auXKlYqLi9PAgQMlSSNHjlTjxo01bty4crd/++239fHHH2vKlCkHqlYAAAAAAA4qe3VMd0ZGhubNm6epU6fKGKO2bdtq2rRpcrvdIbf75JNPdPXVV+v//J//o9atWx/QggEc/BzHUZ06dRgwAquQe9iI3MNG5B7hqn3KsNWrV+v7779XfHy8tm3bJp/Pp6ZNm+qnn37SihUrVFRUpNWrVys3N1cXX3yxXnzxRfXu3Xu/ivP5fIqL+/8lbt68WYcddtheP05JSYni4uLkOI6MMSouLlZ8fLxcrqo/cwgEAvL7/YqPj9/r59x7vClhD8dxQuY/ADYg97ARuYeNyD3CVXul+4svvtCoUaN066236oEHHlBBQYGGDh2qkSNHauTIkZo1a5Y++eQTBQIBZWZmqlmzZntViM/n0/33369NmzZJkmbMmKHOnTsHhxCsXLlSzZo107vvvhu8z44dO+Q4jtLT01W7du1Kv9LT0/X7779Lkv78808lJibK7XYrLi4u+OV2u+U4Tsi2uLg49e3bN/h8brdbhx9+uDIzM9WkSROVlJTIcRw1b948+JWRkaH+/fvv1Wvfze7p5atWrVJKSor+97//lbvOGKOzzz67wsMYcHAKBALasmULUz1hFXIPG5F72IjcI1y1m+7rrrtOGzdu1IYNG7R06VIZY5Senq4FCxZo27Zt+vzzz/Xhhx+qVq1a6t+/v7p06aIZM2ZUvxCXS7/88ovOPPNMZWdnq0ePHlq/fn3wmPBJkybpjDPOUI8ePYL3SU1NlST9/vvvysnJUU5Ojv7++28NGDBAq1evDm4rKChQy5YtJUmHH364CgsLFQgE5PP5gl+9e/fW7bffHrItEAhozpw5wedLSEjQzz//rNtvv109evRQfHy8HMfRH3/8Efx65JFHlJCQUO3Xjd17IgwYMEDXXnutOnToUO76p556SvPmzYtCZYgkY+z+oAl2IvewEbmHjcg9yqr27uWlZs6cqZycHKWnp+uCCy5Q9+7d9fXXXysjIyO4Ujxu3Dideuqp6tOnj9q0aaOTTjppj4/rcrk0depUTZw4UbVq1ZLH49Hbb7+tNm3aaMWKFfrwww/1ww8/hBw/XrrrednjJUpKSvTkk0/q+uuvV6NGjco9j+M4SkxMDNn2ySef6KOPPtJff/1V7vZldy0vfe4FCxZo6NChknY34kcddVTwNjk5OTrnnHP2+Hrx/91///3KycnR/fffX+66X375RXfeeaeOPPLIKFQGAAAAAPtnrwapzZo1S1dddZW+/PJLOY6jxx57TEcffbSGDBlS7rbdunXToEGDNGLEiGo9dsOGDZWZmaknn3xSjRs3VkZGhs455xy1aNFCZ511lowxOvroo5WamqquXbtW+jilu3E0bty4Ws+bnZ2twYMHB3dFX7JkSZWfTBUWFuqrr77SWWedFdy2cuXK4FdFjSMq9+2332rChAnq3bu3Xn/9df3666/B6/x+vwYMGKBhw4apffv2UawSAAAAAPZNtZvuvLw83XTTTXrqqadCGsupU6dq/vz5+uijj8rdZ9iwYfryyy/1zTff7PHx4+LiNG/ePG3evFlZWVmVfj355JMhw9Wk3avXxcXFknY3xS6XK7jreVW8Xq/69u2rI444QtLuVfJrrrlGgwcPrvQYjPnz56tJkyblVsvL4viN6jHG6LrrrlNqaqpcLpd++OEHnXzyycFDCiZMmCCfz6fx48dHt1AccI7jqF69ekz1hFXIPWxE7mEjco9w1d69PDU1VatWrVJmZqZ+++03FRQUSJIyMzM1bNgwjR07Vq+99pr8fn/wPq1atVL79u01a9YsnXjiiVU+/ujRo9W0aVO1adNGmzZtCjku2uv1qqSkRGPHjtWll16qunXrStrdJEtSUVGRmjRpori4OBljZIxRnTp1JO1ugHft2qXCwsKQKYIFBQW6+OKLlZycrPHjx+uCCy5QfHy8Pv30U3Xs2FH9+/fX9OnTy50OrXPnzho7dqxmzpypyy+/XJLUvHlzbdmyRRkZGXK73VVObfd6vfJ6vcHLubm5/+9f9r0ply5dqm+++Ubvvfde8Fj9jh07qm/fvjrllFP06KOP6n//+18NTY9HTXIcJzi8ELAFuYeNyD1sRO4Rbq92L8/MzJS0u3EsLi4ONr1XXnmlPB6PsrKygivOpU455RQtXbp0j489dOhQNWjQINjQ5+Tk6Ndff9XAgQOVkpKioUOHqn///mrTpo169eolaXfj7DiOGjRooPz8fO3cuVNz585Vu3btgkPU5s2bpzp16oQ03H/88Yc6duwot9utN954I6Sxbty4sT799FMtXLhQAwcOLLdqnZqaqscff1xPPPFEyOP17t1bc+fO1bp166r8gGHixIlKT08PfjVt2vT/XWPfsIUNGzYoLi5O5557bnBbhw4d5Pf7deqpp2rixIlq06ZNFCtEpAQCAW3dupW9QmAVcg8bkXvYiNwj3F413aVatmwZPGe3JLVu3Vqff/65WrduraeffjrktoMHD9Z///vfPT5mYWFhuWOpp02bpoULF2rlypV64okn9I9//EPGGBUVFUmSNm7cqLS0tJDzba9YsULt2rULXt6+fbsyMjKCl5cuXap27dqpY8eOeueddyrcTbxly5b64IMP9Pbbb2vBggXlrm/Tpk3wFGSlTjjhBM2cOVOXXHKJ5syZo+3bt1f4OkePHq2dO3cGvzZs2LDH782hqlmzZgoEAiosLAxuW7dunaTdP6xGjx4dPNZ+1qxZevDBB3XMMcdEq1wAAAAA2Gt7Pb1ckjweT0hjK+2ePl67dm1dffXVIdvbtm27x8crKipScnKyHMcJaaBLdxVv0qRJyLamTZvqjz/+0C+//KL69euHPNbbb7+twYMHBy9nZ2cHdzWXpJNPPlnvvvuuzjjjjCprOv744/Xzzz+Xm4D+3XffadKkSbrgggtCtl988cVq3bq13njjjeBKfEU8Hk/IqrvN2rdvrzZt2mjIkCF64IEHtGPHDt10003q2rWrXnzxxZDbjhw5Uk2aNNHIkSOjVC0AAAAA7L19Wuk+0BITE1VcXFzu3NlPP/20evXqFbLN7/frt99+kyR98803at26dfBxFixYoNWrV6tv377BbZs3b1Z6enrwssvl2mPDXSq84fb7/UpNTVXbtm31zDPPhFxXWFioG2+8Uc8++2xwt3tULS4uTh9//LH8fr9OPPFEde7cWU2aNNHMmTPVvHnzkK9atWqpdu3aIR/AAAAAAECsq/ZKd0lJiaZPn66LLroopImdN2+eGjVqpPr166tevXohK9V7o6JhWT6fr8Lblh6D/dFHHwWHlq1du1b9+/fX+PHjQ1aSf/3115Ddyyvi9XpDBsBVxufzqXXr1powYYKk3UPQSkpKdOaZZ6pRo0aaNm2a+vTpow4dOmj8+PHq2bPnHh/z/7Nz0ELTpk312muv7fF2r7zySuSLQY1xuVzKzMzc558XwMGI3MNG5B42IvcIt1dJuOaaa5SdnR2yrVu3bjr66KN12GGHKT4+XmlpaWrSpInatWuniy66SP/73//2uqiVK1fqnHPO0YQJE4LD28ItW7ZM33zzjS644AK99tprOvnkk9W5c2ddf/31kqTly5erXbt2eumll/Y4Ob24uDh4nHhVwhvzX375RZmZmbr66qs1Y8YMJSQk6J133tGFF16oTZs2VfPVlrJvkBrsZYyR3+8vN8cBOJSRe9iI3MNG5B7hqr3SHR8fL2NMuXNkS9L777+vxMREZWdna8uWLdq8ebPWrVun9957T6tWrdJPP/20V0X961//Uo8ePTR06FB17dq1wtvk5uZq8ODB+te//iW3261rr71W9957b/D6Y445RhdeeKHGjRtX5THW0u7TVO3YsWOPdYVPIDzxxBO1bt26kGFscXFxGjt27B4fC7CZMUbZ2dnKzMzkdBqwBrmHjcg9bETuEW6fBqmF+9e//qVmzZqV237XXXdp4sSJ2rp1a6Ur1hVxHEc33nhjlbfp3r27unfvLkk64ogj9MADD4Rc73K5NH78+Go/576qaPo5AAAAAABShAeptW3bVk899ZRSU1Mj+TQAAAAAAMSkvV7pXrp0qe644w61atUquMvEl19+qfj4eDVs2DDktpdddtkBKxTAoYXdrWAjcg8bkXvYiNyjrL1uutesWaPvv/9e3377rQoLC2WM0b///W85jqPGjRvrnHPO0ZAhQ/Y4vAzheGPCHi6XSw0aNIh2GUCNIvewEbmHjcg9wu317uWDBg3SmjVr9PPPP2v9+vWSpJ9++kk//vijxowZozVr1qhDhw66/PLLlZube8ALPnQx3RD2MMbI6/Uy1RNWIfewEbmHjcg9wu33Md2O48jj8aht27a69tprtWjRIr3//vtauHChjjvuOP3+++8Hok4AhxBjjHbs2MEvI1iF3MNG5B42IvcIt19Nd3FxsYwx8vl8IdvPPfdcffnllyoqKlLXrl21cePG/SoSAAAAAICD0V433WWHAjiOo2effVb16tUrd7tmzZrptddeC66EAwAAAABgm70epPbAAw8oPT1djuPIcRy5XC5NnjxZ6enpqlevnho0aKDWrVurZcuWOv300/XZZ59V2JQDsFtc3F7/+AEOeuQeNiL3sBG5R1l7lYYjjzxSK1euVHx8fHCbMUZFRUXKy8vT+vXrlZ+fL8dxVK9ePfXs2VNXXnmlmjZtesALP/QwvRz2cLlcysjIiHYZQI0i97ARuYeNyD3C7VXTvWrVqj3eZuPGjfr666/16aefatasWXrllVc0ffp0ztm9RwxagD2MMSosLFRSUhLnsYQ1yD1sRO5hI3KPcPs9vTxco0aNdMEFF+ipp57S+vXrdeedd6p3794H+mkAHMSMMcrNzWWqJ6xC7mEjcg8bkXuE26em2+fz6bvvvtPKlSvLrX5nZ2dr+vTpkqRatWrpvvvuU1JS0v5XCgAAAADAQWafmu7s7GydeOKJateunfr16xdy3caNG3XdddcdkOIAAAAAADiYVfuY7lmzZqlWrVrq1atXcNuaNWuUmpoacruEhAQlJCQcuAoBHHIcx1FCQgLHOcEq5B42IvewEblHuGqvdN93333q06ePRo4cqYKCAjmOo+bNmystLU1XX321Fi5cGLwtAdsXfM9gD8dxVLduXX5WwCrkHjYi97ARuUe4ajfd//vf/zRp0iT95z//0WmnnRbcXlRUpFdeeUXjxo2LRH0WYdAC7GGMUV5eHgNGYBVyDxuRe9iI3CNctZvutLQ03XLLLfr111919dVXl7t+yZIleu211w5ocQAOTcYY5efn88sIViH3sBG5h43IPcLt9SC14uJiDRs2rNz2wYMH6+abb9aXX34pn8+nRYsW6YsvvtDSpUuVn59/QIoFAAAAAOBgUu2mu6CgQFdddZWOPvpobd++PeSTG8dxNHHiRJWUlGjgwIHatWuXOnfurDPOOEMdO3bUOeecE5HiAQAAAACIZdVuuocMGaI5c+bo5ptvVq1atcoNBqhTp45WrlypxYsXq3bt2srLy1NeXp6eeOIJLVu2TIWFhQe8eAAHJ8dxlJSUxIARWIXcw0bkHjYi9whX7aZ7/Pjx+vrrr3XLLbdUekqwBg0aqF69epKklJQUpaSk6IgjjlBcXJyysrIOTMWHLN6UsIfjOEpPT+eXEaxC7mEjcg8bkXuEq3bT3bJlS7Vt21YFBQUh20tKSiRJgUCgwvu1bdtW69evV9OmTfejTBswaAH2MMZo586dDBiBVcg9bETuYSNyj3B7NUht8eLFatmypT799NPgNq/XK2NMpbuPN2zYUJmZmftXJYBDSunPDH4ZwSbkHjYi97ARuUe4ajfda9as0XnnnaejjjpK7dq1k7R714lGjRrp66+/VnJycsSKBAAAAADgYFTtprt169a66aab9N///leZmZkyxuiKK67QLbfcohNOOCF4zILf75fP54tYwQAAAAAAHCziqn3DuDjdd999kqTExET16dNHLpdLRUVF5W7bsmXLA1chgEOO4zhKSUlhwAisQu5hI3IPG5F7hKt2011Wenq63njjjQqva9u2rb7//vv9KspOvClhD8dxlJqaGu0ygBpF7mEjcg8bkXuE26tBaogkBi3AHsYYbd++nQEjsAq5h43IPWxE7hFun1a6ceAZX0CBEn+0y6gRxmfH60TljDEqLi6WMYZdr2ANcg8bkXvYiNwjHE13jNi+cKUKEhOjXUaNSUpJltvtjnYZAAAAABBRNN0xYsSIEapdu3a0y6gxbrdbcXHEDwAAAMChja4nRiQmJsrj8US7DKBGOI6jtLQ0drmCVcg9bETuYSNyj3A03TGCNyVs4jiOkpOTo10GUKPIPWxE7mEjco9wTC+PEYFAINolADUmEAgoKyuL3MMq5B42IvewEblHOJpuAFHh8/miXQJQ48g9bETuYSNyj7JougEAAAAAiBCabgAAAAAAIoSmO0YwSA02cRxHderUIfewCrmHjcg9bETuEY7p5TGCNyVs4jgOp8iDdcg9bETuYSNyj3CsdMcIphvCJoFAQFu2bCH3sAq5h43IPWxE7hGOphtAVBhjol0CUOPIPWxE7mEjco+yaLoBAAAAAIgQmm4AAAAAACKEpjtGMEgNNnEcR/Xq1SP3sAq5h43IPWxE7hGOpjtG8KaETRzHkdvtJvewCrmHjcg9bETuEY6mO0Yw3RA2CQQC2rp1K7mHVcg9bETuYSNyj3A03QAAAAAAREhctAvAbl6vV16vN9plADUiEAiopKREXq9XLlfNfvbndrsVF8ePPgAAANQM/vKMEVOmTFFiYmK0ywBqTFpamnJzc2v8eT3JKRp58wgabwAAANQI/uqMEd8dc7ZcqenRLgOoMY4xMjU8YMTt9+mk5R/J7/fTdKPGuVwuZWZm1vjeHUA0kXvYiNwjHH91xoiAyyUTFx/tMoCaYYziZeSTIzHZE5Ywxsjv98txHCbawhrkHjYi9wjHxy8AapwjqXGgSPwagk2MMcrOzpYxJtqlADWG3MNG5B7haLoBAAAAAIgQmm4AAAAAACKEphtAVASiXQAQBRzbBxuRe9iI3KMsBqnFCt6YsIhxHK13J0e7DKBGuVwuNWjQINplADWK3MNG5B7hWOmOFQxagE2MUaLxk3tYxRgjr9fLYB1YhdzDRuQe4Wi6AdQ4R9JhAS/Ty2EVY4x27NjBH2GwCrmHjcg9wtF0AwAAAAAQITTdAAAAAABECE03gKgoZnggLBQXx/xS2Ifcw0bkHmWRhlhBAwKLGMfRRicp2mUANcrlcikjIyPaZQA1itzDRuQe4VjpjhUMWoBNjFEt4yP3sIoxRgUFBQzWgVXIPWxE7hGOphtAjXMkZQSKmV4OqxhjlJubyx9hsAq5h43IPcLRdAMAAAAAECE03QAAAAAARAhNN4CoKHT48QO7OI6jhIQEOQzOhEXIPWxE7hGO6eWxgjclLGIcR1ucxGiXAdQox3FUt27daJcB1ChyDxuRe4RjqSlWMGgBNjFGtQPF5B5WMcYoLy+PwTqwCrmHjcg9wtF0A6hxjqTaxsf0cljFGKP8/Hz+CINVyD1sRO4RjqYbAAAAAIAIoekGAAAAACBCaLoBREWe4452CUCNchxHSUlJTLOFVcg9bETuEY7p5bGCNyUsYhxH2Y4n2mUANcpxHKWnp0e7DKBGkXvYiNwjHCvdsYJBC7CIY4zqBbxyyD0sYozRzp07GawDq5B72IjcIxxNN4CoSDX+aJcA1ChjjAoLC/kjDFYh97ARuUc4mm4AAAAAACKEphsAAAAAgAih6QZQ44ykHCdOsbzT1ZYtW7RkyRJt3rw52qXgEOE4jlJSUphmC6uQe9iI3CMcTfd+2LZt24E7VoM3JWziOMpxJUQ198uXL9cpp5wij8ej9PR0jRo1SoFAQJI0btw4/fOf/9SwYcN0+OGH69577y13/ylTpuiMM86o4apxMHMcR6mpqfwRBquQe9iI3CMcTff/c9lll2ncuHHVvr3f71ezZs20atWqA1MAgxZgEccYNQgURW16eV5ens4991ydeeaZ+uOPP/T666/r6aef1quvvqqFCxfqhRde0E8//aTvvvtOH3zwgcaNG6e///47eP+nnnpKt956a1Rqx8HLGKPt27czWAdWIfewEblHOJru/6egoEBut7vS640x8vl8wcvffPONGjRooKOOOiq4zefzqbi4OKJ1AoeKJBOI2nOvXr1a/fv314QJE9SwYUN1795dp59+ur788kslJCTolVdeUcOGDSVJJ554oowx2rFjhyTp//yf/6MZM2Zo8ODBUasfBydjjIqLi/kjDFYh97ARuUc4mu7/x+VyqXbt2pVev23bNtWvX18ZGRnKyMjQGWecoW3btgUvZ2RkKDMzU48++mjNFQ1gn3To0EEPP/xw8LLf79eaNWt0xBFH6LTTTlO3bt0kSSUlJRo7dqzatWuntm3bSpI6deqkJUuWBJtyAAAAoCpx0S4gGtauXSu3262kpKTgtvz8fJWUlASHJvn9fhUUFMjv96t169aqXbt2cKXLGKNjjjlGTzzxhM4888yQxw4EAiouLlZCQkLNvSAA++W5555TYWGhBg4cGNw2f/58XXTRRSopKdH3338vl2v3Z5TNmzePUpUAAAA4GFnZdPfv31+rV68O/hEtSbt27dIXX3wRMjCpuLhYLVq00N13361+/frJ5XLJcRwZY2SM0dlnn13usf1+vzp37qzPPvuswuf2er3yer3By7m5uQfuhQEHCSMpy5UQE9PLV61apdtuu00vvPBCyN4uZ5xxhhYsWKB77rlHF1xwgb777ruQD+qAveU4jtLS0hisA6uQe9iI3COclU33l19+WW7b4YcfrrPPPltTp04td53f79dFF12k+Ph4SdLo0aOVlZWlF198sdxtjTEqKSmp9LknTpyo8ePHl7+CNyVs4jjaFQM/frZv367evXvr6quv1uWXXx5yndvt1vHHH685c+aoXr16WrBggc4///woVYpDgeM4Sk5OjnYZQI0i97ARuUc4jumW9MMPPygvL0/vvPOOsrOzy13vdruDDXd+fr6mT5+u008/Xbt27Sr3JanKXctHjx6tnTt3Br82bNiw+woGLcAijjFqFCiM2vRyaffwxAsvvFAtWrTQ5MmTg9snTJig559/Png5Li5OcXFxVQ5aBKojEAgoKysreGo6wAbkHjYi9wgX/aWmGDBp0iRdc8018nq9GjlypKZNm1bpbTdt2qRNmzZp+PDhuummm4Lbd+3aJb/fr/Xr16tp06aV3t/j8cjj8RzQ+oGDUYIxUpR28DDG6NJLL1VWVpbmz5+voqIiSbs/YDvyyCM1YMAAZWRkqEOHDpo8ebLS09PVuXPn6BSLQ0rZs2AAtiD3sBG5R1nWN90ff/yxPvroI61Zs0YJCQlq27at5s6dq169elV4+1atWik/P1+JiYnBbYsXL9Y555yjqVOnVtlwA4gNK1eu1Pvvvy9JatSoUXB76TyGSZMmadSoUdq2bZtOP/10zZs3j+O5AQAAsE+sbroXLVqkSy65RDNmzFD9+vUlSdOnT1fv3r315ptvqmvXrhXe7+WXX1ZycrL69u2rrKwsXXrppXrllVd08cUX12T5APbR0UcfXeW5M6+//npdf/31VT7GuHHjDnBVAAAAOBRZeUx3fn6+7r77bnXt2lX333+/evbsGbyuS5cuevDBB3X++edr0qRJ8vv95e7v8Xj06KOP6rDDDtNpp52mvn370nADe8FI2uzyxMT0cqCmOI6jOnXqMM0WViH3sBG5RzjrVrq//fZbdevWTfHx8XrnnXd07rnnlrvN9ddfr1q1aunaa6/V/Pnz9fHHH4dcP2jQIA0aNEhfffWVXnjhBb344otavXq1Pvnkk30vjDclbOI4KhKDyWAXx3GY6QHrkHvYiNwjnHVN9wknnKCHHnpIF110kerUqVPp7a644gqdcsopVZ5Hu3379mrfvr0efvhh/fjjj/tXGNPLYRHHGDUNFGqDK0mGD5xgiUAgoG3btql+/fpyuazc0QwWIvewEblHOOuabkkaPHhwtW7XqlWrat2uTp066tSp0/6UBFiHX0GwUVWzBIBDFbmHjcg9yuLvXgAAAAAAIoSmGwAAAACACKHpBlDjjKS/XYlML4dVHMdRvXr1mGYLq5B72IjcIxxNd6zgTQmbOI58csg9rOI4jtxuN3+EwSrkHjYi9whH0x0rGLYAizjG6PBAoRxyD4sEAgFt3bpVgUAg2qUANYbcw0bkHuFougEAAAAAiBCabgAAAAAAIoSmGwAAAACACKHpjhUMWoBFjOPoT1eSDLmHRVwulzIzM+Vy8asX9iD3sBG5RziSECsYKAWbGKM4GXIPqxhj5Pf7Zcg9LELuYSNyj3A03QBqnCOpcaBIrHPDJsYYZWdn80cYrELuYSNyj3A03QAAAAAARAhNNwAAAAAAEULTDSAqAtEuAIgCh+GBsBC5h43IPcqKi3YB+H94Y8IixnG03p0c7TKAGuVyudSgQYNolwHUKHIPG5F7hGOlO1YwaAE2MUaJxk/uYRVjjLxeL4N1YBVyDxuRe4Sj6QZQ4xxJhwW8TC+HVYwx2rFjB3+EwSrkHjYi9whH0w0AAAAAQITQdAMAAAAAECE03QCiopjhgbBQXBzzS2Efcg8bkXuURRpiBQ0ILGIcRxudpGiXAdQol8uljIyMaJcB1ChyDxuRe4RjpTtWMGgBNjFGtYyP3MMqxhgVFBQwWAdWIfewEblHOJpuADXOkZQRKGZ6OaxijFFubi5/hMEq5B42IvcIR9MNAAAAAECE0HQDAAAAABAhNN0AoqLQ4ccP7OI4jhISEuQwOBMWIfewEblHOKaXxwrelLCIcRxtcRKjXQZQoxzHUd26daNdBlCjyD1sRO4RjqY7Rrj8Prl8JdEuA6gZxihdPu1UXI1+4OT2+2rsuYBwxhjt2rVLtWrVYvUD1iD3sBG5Rzia7hhx/A/zlJjIyh/skZaWptzc3Bp/Xk9yitxud40/L2CMUX5+vlJSUvgjDNYg97ARuUc4mu4YMWLECNWuXTvaZQA1IhAIKCsrSxkZGXK5avbYbrfbrbg4fvQBAACgZvCXZ4zweDzyeDzRLgOoEYFAQPHx8fJ4PDXedAMAAAA1ib92YwS7nsAmjuMoKSmJ3MMq5B42IvewEblHOFa6YwRvStjEcRylp6dHuwygRpF72Ijcw0bkHuFY6Y4RxpholwDUGGOMdu7cSe5hFXIPG5F72IjcIxxNd4zgTQmbGGNUWFhI7mEVcg8bkXvYiNwjHE03AAAAAAARQtMNAAAAAECE0HTHCAapwSaO4yglJYXcwyrkHjYi97ARuUc4ppfHCN6UsInjOEpNTY12GUCNIvewEbmHjcg9wrHSHSMYtACbGGO0fft2cg+rkHvYiNzDRuQe4Wi6YwRvStjEGKPi4mJyD6uQe9iI3MNG5B7haLoBAAAAAIgQmm4AAAAAACKEpjtGMEgNNnEcR2lpaeQeViH3sBG5h43IPcIxvTxG8KaETRzHUXJycrTLAGoUuYeNyD1sRO4RjpXuGBEIBKJdAlBjAoGAsrKyyD2sQu5hI3IPG5F7hGOlO0Z4vV55vd7gZbfbrbg4/ntw6PL5fNEuAahx5B42IvewEblHWXR1MWLKlClKTEwMXvakJGvkiJtpvAEAAADgIEZHFyPWnHGynNq1JUlun09Hz1ssv99P0w0AAAAABzE6uhjhi4uTO57/DtjBcRzVqVOHAYKwCrmHjcg9bETuEY4uL1bwpoRFHMeRx+OJdhlAjSL3sBG5h43IPcIxvTxGOMZEuwSgxgQCAW3ZsoWpnrAKuYeNyD1sRO4RjqYbQFQYPmiChcg9bETuYSNyj7JougEAAAAAiBCabgAAAAAAIoSmO0awAwps4jiO6tWrx1RPWIXcw0bkHjYi9whH0x0reFPCIo7jyO1288sIViH3sBG5h43IPcLRdMcIppfDJoFAQFu3bmWqJ6xC7mEjcg8bkXuEo+kGAAAAACBCaLoBAAAAAIgQmm4AAAAAACKEpjtGGAYtwCIul0uZmZlyufgRBHuQe9iI3MNG5B7hSEKsYJAaLGKMkd/vlyH3sAi5h43IPWxE7hGOpjtGsM4NmxhjlJ2dzS8jWIXcw0bkHjYi9whH0w0AAAAAQITQdAMAAAAAECE03QCiwmF4ICxE7mEjcg8bkXuUFRftArAb08thE5fLpQYNGkS7DKBGkXvYiNzDRuQe4VjpjhUMWoBFjDHyer0MGIFVyD1sRO5hI3KPcDTdMYJ1btjEGKMdO3bwywhWIfewEbmHjcg9wtF0AwAAAAAQITTdAAAAAABECE03gKiIi2OOI+xD7mEjcg8bkXuURRpiBNPLYROXy6WMjIxolwHUKHIPG5F72IjcIxwr3bGCQQuwiDFGBQUFDBiBVcg9bETuYSNyj3A03TGCdW7YxBij3NxcfhnBKuQeNiL3sBG5RziabgAAAAAAIoSmuxI+ny/aJQAAAAAADnJWNt1+v1+1atVSQUGBSkpKVFxcLEmaP3++jj/+eEnS6NGjtWDBApWUlFT6OK1bt9YzzzwT0VqffPJJOY4T8jVlypSIPicQaY7jKCEhQQ4DBGERcg8bkXvYiNwjnJXTy91ut7xerxITEzV58mRNmjRJiYmJ8nq92rFjh5o3by5Jev3119WqVSvNnz+/wsdJTk5WUlLSAampsunlS5cu1T333KMRI0YEtx2o5wSixXEc1a1bN9plADWK3MNG5B42IvcIZ2XTLe1uvF0ul2655RYNGjRIkrR48WKNHz9en376qaTdb5j09PRKH6N05fmAqGTQwtKlS3XjjTeqdu3aB+Z5gBhgjNGuXbtUq1YtPgWGNcg9bETuYSNyj3DWNd1lj9UuKSlRbm6uMjIylJ6eLp/Pp6KiIjVv3lzGGMXFxSk7O1t+vz94eU8CgYBKSkrk8Xj2qq6K3o5//fWX1q9frxtuuEE//fSTGjVqpBEjRmj48OF79dhArDHGKD8/XykpKfwygjXIPWxE7mEjco9w1h3T/fDDDys9PV1er1d16tTRzJkzJUnPPfecbrnlFrVo0ULPPfecHnnkkeBu3B9++KHi4+PLHVu9YsUKDRw4MGSb2+3W4YcffkBq/fHHH9WyZUtNnDhR69at07hx4zRq1Cj997//PSCPDwAAAACILOua7tGjRys/P18ej0e7du3SkCFDJElFRUUqLi5WIBBQUVGRvF5v8D7dunXT9u3blZubq7y8vODX0Ucfreeeey5k2/bt27Vq1apKn9/r9So3NzfkqzLnnnuu1q5dq3PPPVcNGzbUgAEDdOmll2r69OkH7hsCAAAAAIgY63YvD1d60vpHHnlE+fn52rJlix555BH5/f7g5PKEhAQlJCSUu6/L5ZLH41GtWrWq/XwTJ07U+PHj97neRo0a6bPPPtvn+wOxwHEcJSUlscsVrELuYSNyDxuRe4SzbqU7XCAQ0GmnnaaVK1dq5syZ6tWrl1auXKkePXrommuuOeDPN3r0aO3cuTP4tWHDBkkVTy+/77779OCDD4ZsW7x4sZo1a3bA6wJqUumQQn4ZwSbkHjYi97ARuUc461e6W7RooeLi4pDp4LVr11ZBQYHi4+P11FNP6bHHHtPVV199QJ7P4/FUOGTNqWB6efv27TVgwAC1bt1aLVq00Kuvvqply5Zp4cKFB6QWIFqMMcrNzVVaWhq/kGANcg8bkXvYiNwjnPUr3WvXrtWff/6p888/X+vXr1dOTo5ycnJ02WWX6fnnn9fmzZt1xRVXRKW27t27a8KECbrpppt02mmn6euvv9bChQvVqVOnqNQDHCjGGBUWFgYP7wBsQO5hI3IPG5F7hLN+pTstLU2SlJOTo4cfflj33XefpN2D1RzHUWJioiSpoKBA27ZtU0JCQvATK5/Pp507d2rz5s2SpOLiYrlcLjVp0uSA1Td06FANHTr0gD0eAAAAAKDmWNl0G2NUUlIir9cb3NV7+vTpqlWrlnw+n0488UT9/PPPuv3224P3WbZsmbp3766EhAS5XP9/B4ExY8ZozJgxknaf97tDhw764osvavYFAQAAAABikpVNt+M48vv9Idvq1q0b/PfLL7+sZs2aKSMjI7jtrLPOks/ni1hN7HwCmziOo5SUFI5zglXIPWxE7mEjco9wVjbde3L88cfX/JPypoRFHMdRampqtMsAahS5h43IPWxE7hHO+kFqsaKi6eXAocoYo+3btzNgBFYh97ARuYeNyD3C0XQDqHHGGBUXF/PLCFYh97ARuYeNyD3C0XQDAAAAABAhNN0AAAAAAEQITXeMYOcT2MRxHKWlpTHVE1Yh97ARuYeNyD3CMb08VvCmhEUcx1FycnK0ywBqFLmHjcg9bETuEY6V7hjB9HLYJBAIKCsrS4FAINqlADWG3MNG5B42IvcIR9MNICp8Pl+0SwBqHLmHjcg9bETuURZNNwAAAAAAEULTDQAAAABAhNB0xwiO6IZNHMdRnTp1mOoJq5B72Ijcw0bkHuGYXh4reFPCIo7jyOPxRLsMoEaRe9iI3MNG5B7hWOmOEUwvh00CgYC2bNnCVE9YhdzDRuQeNiL3CEfTDSAqDB80wULkHjYi97ARuUdZNN0AAAAAAEQITTcAAAAAABFC0x0j2AEFNnEcR/Xq1WOqJ6xC7mEjcg8bkXuEo+mOFbwpYRHHceR2u/llBKuQe9iI3MNG5B7haLpjBNPLYZNAIKCtW7cy1RNWIfewEbmHjcg9wtF0AwAAAAAQITTdAAAAAABECE03AAAAAAARQtMdIwyDFmARl8ulzMxMuVz8CII9yD1sRO5hI3KPcCQhVjBIDRYxxsjv98uQe1iE3MNG5B42IvcIR9MdI1jnhk2MMcrOzuaXEaxC7mEjcg8bkXuEo+kGAAAAACBCaLoBAAAAAIgQmm4AUeEwPBAWIvewEbmHjcg9yoqLdgHYzeX3y1XikyS5fb4oVwNElsvlUoMGDaJdBlCjyD1sRO5hI3KPcDTdMaLtwmVKTEwMXvakJMvtdkexIiByjDEqLi5WQkICnwTDGuQeNiL3sBG5Rzia7hgxYsQI1a5dO3jZ7XYrLo7/HhyajDHasWOHMjMz+WUEa5B72Ijcw0bkHuHo6mKEx+ORx+OJdhkAAAAAgAOIQWoAAAAAAEQITTeAqODwCdiI3MNG5B42IvcoizTECJeLzz9gD5fLpYyMjGiXAdQocg8bkXvYiNwjHJ1ejDDGRLsEoMYYY1RQUEDuYRVyDxuRe9iI3CMcTXeM4E0JmxhjlJubS+5hFXIPG5F72IjcIxxNNwAAAAAAEULTDQAAAABAhNB0xwjHcaJdAlBjHMdRQkICuYdVyD1sRO5hI3KPcEwvjxG8KWETx3FUt27daJcB1ChyDxuRe9iI3CMcK90xgkELsIkxRnl5eeQeViH3sBG5h43IPcLRdMcI3pSwiTFG+fn55B5WIfewEbmHjcg9wtF0AwAAAAAQITTdAAAAAABECE13jGCQGmziOI6SkpLIPaxC7mEjcg8bkXuEY3p5jOBNCZs4jqP09PRolwHUKHIPG5F72IjcIxwr3TGCQQuwiTFGO3fuJPewCrmHjcg9bETuEY6mO0bwpoRNjDEqLCwk97AKuYeNyD1sRO4RjqYbAAAAAIAI4ZjuKCv9BCw3N1cuF5+BwA6BQEB5eXlKTEwk97AGuYeNyD1sRO7tkZubK2nPey3TdEdZdna2JOnwww+PciUAAAAAgL2Vl5dX5fA8mu4oq1u3riRp/fr1TDmENXJzc9W0aVNt2LBBaWlp0S4HqBHkHjYi97ARubeHMUZ5eXlq1KhRlbej6Y6y0l1O0tPTeVPCOmlpaeQe1iH3sBG5h43IvR2qs3DKQQYAAAAAAEQITTcAAAAAABFC0x1lHo9H99xzjzweT7RLAWoMuYeNyD1sRO5hI3KPcI7hrO0AAAAAAEQEK90AAAAAAEQITTcAAAAAABFC0w0AAAAAQITQdEfRypUrddJJJ6lOnToaNWqUOLweh4p3331X//jHPxQXF6djjz1Wa9askVR15j///HMdeeSRysjI0GOPPRat0oED4pxzztErr7wiqepsv/HGGzr88MPVqFEj/ec//4lCpcCBcfvtt6tnz57By/y8x6Fs6tSpatq0qZKTk3XGGWfo999/l0TuUTma7ijxer3q2bOnTjjhBH3zzTdavXp18A804GD222+/aeDAgXrwwQf1999/65///KcGDx5cZea3bdumXr16qV+/flq2bJlmzpyphQsXRveFAPto5syZ+vjjjyVVne2VK1fq8ssv1913362PP/5YY8eO1c8//xzN0oF98sMPP+iZZ57R448/Lqnqv3H4eY+D3W+//aZ7771X7777rn766Se1bNlSV111FblH1Qyi4u233zZ16tQx+fn5xhhjli9fbk477bQoVwXsv/fee888//zzwcsLFiwwSUlJVWZ+8uTJ5ogjjjCBQMAYY8w777xjLr/88povHthP2dnZpkGDBqZNmzZm2rRpVWb7pptuMt27dw/ed8qUKWbMmDFRqRvYV36/33To0MHcfffdwW38vMehbM6cOaZv377By4sXLzYNGzYk96gSK91RsmLFCp188slKTk6WJB1zzDFavXp1lKsC9l+PHj00ZMiQ4OWff/5ZrVu3rjLzK1as0JlnninHcSRJ7du317ffflvzxQP76dZbb1Xv3r118sknS6o62ytWrFCXLl2C9yX3OBg999xz+vHHH9W8eXPNnTtXxcXF/LzHIa1t27ZasGCBli9frp07d+qZZ57R2WefTe5RJZruKMnNzVWLFi2Clx3Hkdvt1o4dO6JYFXBgFRcX69FHH9V1111XZebDr0tLS9PGjRujUTKwzxYuXKj58+dr0qRJwW1VZZvc42C3a9cu3XPPPfrHP/6hP//8U5MnT9bpp5/Oz3sc0tq2bauLL75Yxx13nGrXrq1ly5bpkUceIfeoEk13lMTFxcnj8YRsS0xMVEFBQZQqAg68e+65RykpKRo8eHCVmQ+/jvcCDjZFRUW69tpr9eyzzyo1NTW4vapsk3sc7N566y3l5+dr4cKFGj9+vD799FPl5eXp5Zdf5uc9DllfffWV3nvvPX355ZfKyclRv379dN555/F3DqpE0x0ldevW1bZt20K25eXlKSEhIUoVAQfWggUL9PTTT2vWrFmKj4+vMvPh1/FewMHmvvvu00knnaTzzz8/ZHtV2Sb3ONj99ddfOvnkk5WRkSFp9wdJxxxzjHJycvh5j0PWf/7zH/373/9Whw4dlJ6ervvvv1+//fYbf+egSjTdUXLSSSdp2bJlwcvr1q2T1+tV3bp1o1gVcGCsW7dO/fr109NPP622bdtKqjrz4dd9//33aty4cY3XDeyrWbNm6d1331Xt2rVVu3ZtzZo1S0OHDtWrr75aabbJPQ52TZo0UWFhYci2P//8U1OmTOHnPQ5ZgUBAW7duDV7Oy8sLrmaTe1SGpjtKOnXqpNzcXE2bNk2SNGHCBHXt2lVutzvKlQH7p7CwUD169NAFF1yg3r17a9euXdq1a5c6duxYaeZ79eqlJUuWaN68eSopKdGkSZPUvXv3KL8SoPoWLVqklStXavny5Vq+fLl69eqle++9V+vXr6802xdddJFmz56tH3/8Ubt27dITTzxB7nFQOf/887V69Wo999xz+uuvv/TEE09oxYoV6tOnDz/vccjq2LGj3nrrLU2ePFmzZs3ShRdeqMMOO0zDhw8n96hctMen2+zdd981ycnJpl69eqZ+/fpm1apV0S4J2G/vvPOOkVTua926dVVm/tlnnzXx8fGmTp06pkWLFmbz5s1RfBXA/rnyyivNtGnTjDFVZ/vOO+80CQkJJi0tzZxwwgmmoKAgShUD+2bx4sXm5JNPNklJSeYf//iHmTt3rjGm6r9x+HmPg1kgEDD33nuvadasmYmPjzfHHXec+e6774wx5B6Vc4wxJqpdv+U2b96sb7/9VieffLLq1asX7XKAiKsq8+vWrdNPP/2kjh07qlatWlGqEDjwqsr26tWr9ffff6tz584c44dDCj/vYSNyj4rQdAMAAAAAECEc0w0AAAAAQITQdAMAAAAAECE03QAAAAAARAhNNwAAAAAAEULTDQAAAABAhNB0AwBgmZycHJWUlAQvT506VQ8//HClty8pKZHX69X+nPCkqKhIkrRx40Y98sgjKi4uVkFBQfAxly9frt9++22fHx8AgFhF0w0AgGUeeOABnXDCCcGGd+vWrZo4caIKCwsrvP3zzz+vxMREuVwuOY5Tra9Ro0YF75+fn6+jjjpKCxcu1IYNGzRq1CgFAgFNmDBBAwcOlCRNnjxZ/fr122PtcXFxat26tY499thKvxITE/Xqq68egO8UAAD7Ly7aBQAAgJpTWFioadOm6eabb5bjOJKkIUOG6P7779e0adM0dOjQcvfp37+/zj33XHk8HsXF7flPh3/9619KTEwMXk5JSdHYsWM1ZMgQzZw5U5JUXFysF154QXPnzpUkffPNN7ryyiv3+NhJSUk677zz1KJFi0pvM3HiRCUlJe3xsQAAqAk03QAAWGTSpEmSpOHDhwe3ZWRkaNiwYXrooYd09dVXhzTMfr9f8fHxOvzwwyttuI0xwd3VExIS5DiO3G538Povv/xSOTk5uuKKK/Tee+9JkmbMmKFu3brpyy+/VO3atbV69WqdcsopysnJkSQFAgG5XC7Vrl273POlp6crIyOj0tdY9rkBAIg2mm4AACyxbt06Pfjgg3r00UeVmpoact2dd96pl19+WRMmTNC9994b3L5w4UKdffbZ1Xr8MWPG6P777y+3fe3atfrvf/8rn8+n+fPnS5IWLFigQCCgTz/9VAUFBZKkTp06hdxv0KBBmjp1asg2n8+n2bNnKzk5udI6du7cKa/XW62aAQCINMfsz1QUAABwUAgEAjrrrLOUk5Ojb7/9Vi5X+bEus2bN0oABA/TBBx+oe/fukqSCggJt3rxZiYmJSkhIqPCxjTHyer1KSkpSvXr1lJGRoRtvvFHjxo0L3qagoEAXXXSR/vrrL61cuVJTpkzRP//5T5177rk666yz1KhRI02fPl2S1L17d3Xu3FkjR45UQkKCfD6fvF6vkpOTg7vEV0fp8LeyK/cAANQ0VroBALDAXXfdpaVLl+p///ufXC6X/H6/rrvuOo0YMUL/+te/JEmXXXaZ5s+frz59+ui///2vOnfurOTkZP3jH//Yr+f++uuvNWjQIDVt2lTPP/+8TjvtNB155JHq27evnn32WX322Wc6//zzg7fftGmTGjRoEGzyly5dqrPPPlsJCQlKTU2tcpVb2v0hQG5urnbt2qU777xTd999937VDwDA/qDpBgDgEPf4449r4sSJmjx5so499lhJ0ieffKKpU6eqe/fuwaZbkp566in9/vvvOvvss/Xoo49q2LBhkqSzzz5b8+bNq/Q5pk6dqkGDBlV43UcffaRevXpp/PjxmjFjhq644gp17txZs2fP1syZM1W/fn19//33wdtv2rQpZFBap06d5PV6lZeXp5deeqlar7lDhw465ZRTqnVbAAAiiVOGAQBwCCsoKNDs2bODq9qlpkyZos6dO+viiy8OuX1SUpI++OAD9e7dW+ecc05we7169dSnTx/t2LEj5CsrK0uSqtyF+8orr9SAAQO0du1aDRkyRMcdd5y2bdumo48+WrVr19aLL76orVu36vfff1dWVpaysrJCPggolZ+fr5tvvlnx8fFq3ry5mjdvrrVr1+qFF14IXm7evLmeffbZ4LHjAABEGyvdAAAcwpKTk/Xxxx+rVq1awW1fffWVPv30U3311VfBbUVFRdq6dauaNWum5ORkvfbaayGPUzq5PHyauM/nk1T1xPBbb71Vc+fOVSAQkDFGd999t+6++24VFBTooYceUs+ePdW5c2e9/fbbatWqlVq1aqUGDRqUe5zS3c0nTZqk+Ph4SVJubq7y8vI0cuTI4O3++uuvap3aDACAmsBvJAAADnFpaWkhl8eMGaO+ffvqxBNPDG4bMmSIfv31Vy1ZsqTCIWt7UtVc1jlz5kiSTjzxRA0cOFA33HCDfv/9dx1xxBHBlfb+/fvr3nvv1XHHHVfptPSioiJJ0r///W/Vr19f0u4PEL7++mtdd911wds9+eSTwQ8DAACINppuAAAsMnfuXH3xxRdavXp1yPZ77rlHxxxzjJ5//nldf/315e5njNFrr71WbgW8VHFxcZXP++677+rbb7/VGWecoU8++UTTpk1T3759g8du//vf/9Ydd9yhN954Qz/88EOFj1G7dm395z//Cdn266+/yuPxqEmTJsFtDz30UIW7pwMAEA003QAAWGLTpk26/vrrdcstt6hly5YqKirSpk2btGHDBv3111869thjNXr0aPXp06fc7t35+fnq06dPuUFmgUBAw4cPV7NmzUK2hTv//PO1ZMkSzZs3T1deeaU2b96s008/XfPmzVPXrl2Vm5urWrVqKSEhQbm5uRXWP378eM2cOTPk+PHS3cvvuuuu4Lbi4mIddthhIbvPAwAQLTTdAABY4I8//lDnzp21ceNGvf7663ruueeUk5MjSUpJSVHLli3VqlUr/fzzz7r99tv1yiuvhNy/a9euSktLK3dMtyTNmDFDmzdv1rvvvqs//vhDO3bskMfjCblNXFycjj76aL333nvyer2aM2eOfvjhB3388cdKTk7W5ZdfrpNOOkmnn366zjnnHL355pvq1q1byGPccccdGj16tNLT04Pn6546dapeeeUVLV68OHg7v98f3BUdAIBoc0xVB2EBAIBDQiAQ0LHHHquMjAwdd9xxatu2rVq3bq3WrVurYcOGwds9/fTTGjZsmNasWaM2bdpU+/Fzc3PVrFkz7dy5U8cff7zmzJkTPL/30qVL9eKLL+qtt97S2WefrcmTJ6tp06batm2bRo0apVmzZunWW2/VAw88IL/fr/79+2vOnDl66KGHNGrUKEnSUUcdpa1bt5Y7Pt3r9aqoqEjp6ekh20tKSrRz507NmjVL55133r5+2wAA2G+sdAMAYAGXy6UVK1YEV4grM2TIELVo0WKvGm5p97C2zz77TI0bNw4OOSvVokULpaena+nSpSHHWmdkZKh169b66quvgucPd7lceu2119S9e3dddNFFwduuXLlyr+oBACBWsNINAAAAAECE7P05QQAAAAAAQLXQdAMAAAAAECE03QAAAAAARAhNNwAAAAAAEULTDQAAAABAhNB0AwAAAAAQITTdAAAAAABECE03AAAAAAARQtMNAAAAAECE/F/t/6OSjtbfAAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# --- 全局设置：解决中文显示问题 ---\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# （4）统计不同学历岗位的公司需求量（你的原有代码）\n",
    "edu_company_count = job_copy.groupby('edu_merged')['company_name'].nunique()\n",
    "print(\"\\n不同学历岗位公司需求量：\")\n",
    "print(edu_company_count)\n",
    "\n",
    "# --- 核心：简洁直观的可视化图表（横向柱状图） ---\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# 设置简洁配色（单色系渐变，清晰不杂乱）\n",
    "colors = ['#4ECDC4', '#45B7D1', '#96CEB4', '#FECA57', '#FF9FF3']\n",
    "# 绘制横向柱状图（barh，y轴为学历，x轴为公司数量）\n",
    "bars = plt.barh(\n",
    "    edu_company_count.index,\n",
    "    edu_company_count.values,\n",
    "    color=colors[:len(edu_company_count)],\n",
    "    edgecolor='gray',\n",
    "    linewidth=0.8\n",
    ")\n",
    "\n",
    "# 添加数值标签（在柱子右侧，直观可读）\n",
    "for bar, count in zip(bars, edu_company_count.values):\n",
    "    plt.text(\n",
    "        bar.get_width() + 0.5,  # 数值在柱子右侧\n",
    "        bar.get_y() + bar.get_height()/2,  # 垂直居中\n",
    "        f'{int(count)}',\n",
    "        ha='left', va='center',\n",
    "        fontsize=11,\n",
    "        fontweight='bold'\n",
    "    )\n",
    "\n",
    "# 简化图表样式，突出核心数据\n",
    "plt.title('不同学历岗位的公司需求量', fontsize=14, pad=15)\n",
    "plt.xlabel('公司数量', fontsize=12)\n",
    "plt.ylabel('学历类型', fontsize=12)\n",
    "plt.grid(axis='x', linestyle='--', alpha=0.3)  # 仅保留x轴网格线，辅助读数\n",
    "plt.tight_layout()  # 自动调整布局，避免标签截断\n",
    "\n",
    "# 保存图表到home目录（可选）\n",
    "save_path = os.path.join(os.path.expanduser(\"~\"), \"不同学历公司需求量.png\")\n",
    "plt.savefig(save_path, dpi=600, bbox_inches='tight')\n",
    "print(f\"\\n图表已保存至: {save_path}\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "741dbf55-d874-4474-a7a1-bb0aee95e771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "图表已保存至: C:\\Users\\86151\\数据分析岗位不同学历要求占比.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# （5）绘制不同学历要求分布图（饼图）\n",
    "plt.figure(figsize=(10, 7))\n",
    "edu_company_count.plot(kind='pie', autopct='%1.1f%%', startangle=90, \n",
    "                       colors=['#ff9999', '#66b3ff', '#99ff99', '#ffcc99'])\n",
    "plt.title('数据分析岗位不同学历要求占比', fontsize=14)\n",
    "plt.ylabel('')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 保存图表到home目录（可选）\n",
    "save_path = os.path.join(os.path.expanduser(\"~\"), \"数据分析岗位不同学历要求占比.png\")\n",
    "plt.savefig(save_path, dpi=600, bbox_inches='tight')\n",
    "print(f\"\\n图表已保存至: {save_path}\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce0cffa8-e012-4b4e-badd-3a61473a5a65",
   "metadata": {},
   "source": [
    "3.3 为了更好地了解不同城市“数据分析”岗位的薪资水平，我们需获得不同城市的“数据分析”岗位的薪资平均值。\n",
    "（1）以“city”列进行分组计算出“salary”列的均值，降序排列\n",
    "（2）使用reshape(-1)方法将salary_df的数据从二维降为一维\n",
    "（3）绘制不同城市“数据分析”岗位平均薪资柱形图\n",
    "（4）使用聚合函数查看不同城市“数据分析”岗位薪资情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c18f7e4c-3f8c-4ed7-97bd-352433d9ddc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "各城市平均薪资（降序）：\n",
      "city\n",
      "北京    22.682131\n",
      "深圳    20.670068\n",
      "上海    19.353448\n",
      "杭州    19.105536\n",
      "广州    12.631757\n",
      "南京    10.679245\n",
      "武汉     9.869369\n",
      "成都     9.233607\n",
      "西安     7.411290\n",
      "Name: salary_avg, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# （1）按城市分组计算平均薪资，降序排列\n",
    "city_salary = job_copy.groupby('city')['salary_avg'].mean().sort_values(ascending=False)\n",
    "print(\"\\n各城市平均薪资（降序）：\")\n",
    "print(city_salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2df494e2-5915-4f91-92c6-421aef77e8e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "各城市平均薪资（降序）：\n",
      "city\n",
      "北京    22.682131\n",
      "深圳    20.670068\n",
      "上海    19.353448\n",
      "杭州    19.105536\n",
      "广州    12.631757\n",
      "南京    10.679245\n",
      "武汉     9.869369\n",
      "成都     9.233607\n",
      "西安     7.411290\n",
      "Name: salary_avg, dtype: float64\n",
      "\n",
      "图表已保存至: C:\\Users\\86151\\各城市平均薪资分布图.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# --- 全局设置：解决中文显示问题+优化图表默认样式 ---\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams['axes.spines.top'] = False  # 隐藏上边框\n",
    "plt.rcParams['axes.spines.right'] = False  # 隐藏右边框\n",
    "\n",
    "# （1）按城市分组计算平均薪资（你的原有代码）\n",
    "city_salary = job_copy.groupby('city')['salary_avg'].mean().sort_values(ascending=False)\n",
    "print(\"\\n各城市平均薪资（降序）：\")\n",
    "print(city_salary)\n",
    "\n",
    "# --- 核心：修改数值标签偏移量，贴近柱子顶部 ---\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# 生成渐变色（从深蓝到浅蓝，视觉统一高级）\n",
    "n_cities = len(city_salary)\n",
    "colors = [plt.cm.Blues(0.3 + i/n_cities * 0.6) for i in range(n_cities)]\n",
    "\n",
    "# 绘制纵向柱状图\n",
    "bars = plt.bar(\n",
    "    city_salary.index,\n",
    "    city_salary.values,\n",
    "    color=colors,\n",
    "    edgecolor='white',\n",
    "    linewidth=1\n",
    ")\n",
    "\n",
    "# 关键修改：减小y轴偏移量（将+50改为+5~+20，根据薪资范围调整）\n",
    "# 这里设为+10，若薪资单位是千元可改为+0.5，灵活适配数据量级\n",
    "for bar, salary in zip(bars, city_salary.values):\n",
    "    plt.text(\n",
    "        bar.get_x() + bar.get_width()/2,\n",
    "        bar.get_height() + 1,  # 偏移量从50改为10，数字紧贴柱子\n",
    "        f'{salary:.1f}',\n",
    "        ha='center', va='bottom',\n",
    "        fontsize=10,\n",
    "        fontweight='bold'\n",
    "    )\n",
    "\n",
    "# 精简美化图表细节\n",
    "plt.title('各城市数据分析岗位平均薪资', fontsize=15, pad=20, fontweight='bold')\n",
    "plt.xlabel('城市', fontsize=12)\n",
    "plt.ylabel('平均薪资（元）', fontsize=12)\n",
    "plt.xticks(rotation=45, ha='right', fontsize=10)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.2, color='gray')\n",
    "plt.tight_layout()\n",
    "\n",
    "# 保存图表到home目录（可选）\n",
    "save_path = os.path.join(os.path.expanduser(\"~\"), \"各城市平均薪资分布图.png\")\n",
    "plt.savefig(save_path, dpi=600, bbox_inches='tight')\n",
    "print(f\"\\n图表已保存至: {save_path}\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a8643db4-33cb-43c0-b14c-0d6cfc95afad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# （2）将薪资数据从二维降为一维\n",
    "city_salary_1d = city_salary.values.reshape(-1)\n",
    "city_names = city_salary.index.tolist()  \n",
    "#主要是数据格式的转化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "98a7b28c-096f-4f2e-952f-078ffb840f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# （3）绘制不同城市平均薪资柱形图\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(city_names, city_salary_1d, color='#2ca02c')\n",
    "plt.title('各城市数据分析岗位平均薪资', fontsize=14)\n",
    "plt.xlabel('城市', fontsize=12)\n",
    "plt.ylabel('平均薪资（k）', fontsize=12)\n",
    "plt.xticks(rotation=45)\n",
    "# 在柱子上添加薪资数值\n",
    "for i, v in enumerate(city_salary_1d):\n",
    "    plt.text(i, v + 0.5, f'{v:.1f}', ha='center')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "70f1b859-c9ab-4cf4-b24f-8a9b87752ef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "各城市薪资详细情况：\n",
      "      平均薪资  薪资中位数  最高薪资\n",
      "city                   \n",
      "上海    19.4   18.0  55.0\n",
      "北京    22.7   22.5  55.0\n",
      "南京    10.7    7.0  65.0\n",
      "广州    12.6   10.2  45.0\n",
      "成都     9.2    8.0  30.0\n",
      "杭州    19.1   16.5  75.0\n",
      "武汉     9.9    8.0  30.0\n",
      "深圳    20.7   19.8  60.0\n",
      "西安     7.4    6.5  18.0\n"
     ]
    }
   ],
   "source": [
    "# （4）使用聚合函数查看不同城市薪资的详细情况（均值、中位数、最大值）\n",
    "city_salary_detail = job_copy.groupby('city')['salary_avg'].agg(['mean', 'median', 'max']).round(1)\n",
    "city_salary_detail.columns = ['平均薪资', '薪资中位数', '最高薪资']\n",
    "print(\"\\n各城市薪资详细情况：\")\n",
    "print(city_salary_detail)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75a7b012-6754-41b4-834d-552e33ec9af0",
   "metadata": {},
   "source": [
    "对于这种多指标（均值、中位数、最大值）在多个类别（城市）上的对比数据，最合适的图表类型是分组柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "2f9b2425-d470-4d8f-8c89-52a76d75e4e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "各城市薪资详细情况：\n",
      "      平均薪资  薪资中位数  最高薪资\n",
      "city                   \n",
      "上海    19.4   18.0  55.0\n",
      "北京    22.7   22.5  55.0\n",
      "南京    10.7    7.0  65.0\n",
      "广州    12.6   10.2  45.0\n",
      "成都     9.2    8.0  30.0\n",
      "杭州    19.1   16.5  75.0\n",
      "武汉     9.9    8.0  30.0\n",
      "深圳    20.7   19.8  60.0\n",
      "西安     7.4    6.5  18.0\n",
      "\n",
      "图表已保存至: C:\\Users\\86151\\各城市薪资详情分组柱状图.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np  # 确保导入了 numpy\n",
    "import os\n",
    "\n",
    "# --- 全局设置 ---\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams['axes.spines.top'] = False\n",
    "plt.rcParams['axes.spines.right'] = False\n",
    "\n",
    "# （4）你的数据聚合代码（保持不变）\n",
    "city_salary_detail = job_copy.groupby('city')['salary_avg'].agg(['mean', 'median', 'max']).round(1)\n",
    "city_salary_detail.columns = ['平均薪资', '薪资中位数', '最高薪资']\n",
    "print(\"\\n各城市薪资详细情况：\")\n",
    "print(city_salary_detail)\n",
    "\n",
    "# --- 核心：多指标分组柱状图可视化（已修正） ---\n",
    "cities = city_salary_detail.index.tolist()\n",
    "metrics = city_salary_detail.columns.tolist()\n",
    "n_cities = len(cities)\n",
    "n_metrics = len(metrics)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "bar_width = 0.25  \n",
    "index = range(n_cities)  # index 仍然是 range 对象\n",
    "\n",
    "colors = ['#3498db', '#e74c3c', '#2ecc71']\n",
    "\n",
    "# 修正发生在这个循环内部\n",
    "for i, (metric, color) in enumerate(zip(metrics, colors)):\n",
    "    # 修正点：将 range 对象 index 转换为 numpy 数组后再进行加法运算\n",
    "    bar_offset = np.array(index) + i * bar_width\n",
    "    \n",
    "    # 使用修正后的 bar_offset 来绘制柱子\n",
    "    bars = ax.bar(bar_offset, city_salary_detail[metric], bar_width, color=color, label=metric)\n",
    "\n",
    "    # 为柱子添加数值标签（这部分代码不变）\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        ax.text(bar.get_x() + bar.get_width()/2., height + 1,\n",
    "                f'{height:.0f}',\n",
    "                ha='center', va='bottom', fontsize=9)\n",
    "\n",
    "# 后续的图表美化代码（保持不变）\n",
    "ax.set_title('各城市数据分析岗位薪资详情对比', fontsize=16, pad=20, fontweight='bold')\n",
    "ax.set_xlabel('城市', fontsize=14)\n",
    "ax.set_ylabel('薪资（元）', fontsize=14)\n",
    "ax.set_xticks([i + bar_width for i in index])\n",
    "ax.set_xticklabels(cities, rotation=45, ha='right', fontsize=11)\n",
    "ax.legend(fontsize=12)\n",
    "ax.grid(axis='y', linestyle='--', alpha=0.3)\n",
    "ax.set_ylim(0, city_salary_detail.values.max() * 1.15)\n",
    "\n",
    "plt.tight_layout()\n",
    "save_path = os.path.join(os.path.expanduser(\"~\"), \"各城市薪资详情分组柱状图.png\")\n",
    "plt.savefig(save_path, dpi=600, bbox_inches='tight')\n",
    "print(f\"\\n图表已保存至: {save_path}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14aee906-bf44-483f-9dca-c694b4850aaf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c01f48a-9c12-42c6-bd34-e9328bd479ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1919efd-09aa-4b45-bce6-690e34fa54c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b98314ec-7296-462e-9ea5-d2c6e6e5c323",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0956f78-a136-4436-805d-7f69f7d298ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1200277-b7af-4f60-8b0c-bab42f80771c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
